Supervised Learning : Exploring Boosting, Bagging and Stacking Algorithms for Ensemble Learning
John Pauline Pineda
August 23, 2023
- 1.
Table of Contents
- 1.1 Sample Data
- 1.2 Data Quality Assessment
- 1.3 Data Preprocessing
- 1.4 Data Exploration
- 1.5 Model Boosting
- 1.6 Model Bagging
- 1.7 Model Ensemble and Stacking
- 1.7.1 Base Learner Model Development using Linear Discriminant Analysis (BAL_LDA)
- 1.7.2 Base Learner Model Development using Classification and Regression Trees (BAL_CART)
- 1.7.3 Base Learner Model Development using Support Vector Machine - Radial Basis Function Kernel (BAL_SVM_R)
- 1.7.4 Base Learner Model Development using K-Nearest Neighbors (BAL_KNN)
- 1.7.5 Base Learner Model Development using Naive Bayes (BAL_NB)
- 1.7.6 Ensemble Learner Model Development using Linear Regression (ENL)
- 1.7.7 Meta Learner Model Development using Linear Regression (MEL_LR)
- 1.7.8 Meta Learner Model Development using Random Forest (MEL_RF)
- 1.8 Algorithm Comparison Summary
- 2. References
1. Table of Contents
Details.
Details.
1.1 Sample Data
The Wisconsin Breast Cancer dataset shared from the Kaggle website as obtained from the UCI Machine Learning Repository was used for this illustrated example.
Preliminary dataset assessment:
[A] 1138 rows (observations)
[B] 32 columns (variables)
[B.1] 1/32 metadata (unique identifiers) = id (numeric)
[B.2] 1/32 response = diagnosis (factor)
[B.3] 30/32 predictors = 30/30 numeric
[B.3.1] radius_mean (numeric)
[B.3.2] texture_mean (numeric)
[B.3.3] perimeter_mean (numeric)
[B.3.4] area_mean (numeric)
[B.3.5] smoothness_mean (numeric)
[B.3.6] compactness_mean (numeric)
[B.3.7] concavity_mean (numeric)
[B.3.8] concave.points_mean (numeric)
[B.3.9] symmetry_mean (numeric)
[B.3.10] fractal_dimension_mean (numeric)
[B.3.11] radius_se (numeric)
[B.3.12] texture_se (numeric)
[B.3.13] perimeter_se (numeric)
[B.3.14] area_se (numeric)
[B.3.15] smoothness_se (numeric)
[B.3.16] compactness_se (numeric)
[B.3.17] concavity_se (numeric)
[B.3.18] concave.points_se (numeric)
[B.3.19] symmetry_se (numeric)
[B.3.20] fractal_dimension_se (numeric)
[B.3.21] radius_worst (numeric)
[B.3.22] texture_worst (numeric)
[B.3.23] perimeter_worst (numeric)
[B.3.24] area_worst (numeric)
[B.3.25] smoothness_worst (numeric)
[B.3.26] compactness_worst (numeric)
[B.3.27] concavity_worst (numeric)
[B.3.28] concave.points_worst (numeric)
[B.3.29] symmetry_worst (numeric)
[B.3.30] fractal_dimension_worst (numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(tidyr)
library(caret)
library(lattice)
library(dplyr)
library(moments)
library(skimr)
library(RANN)
library(pls)
library(corrplot)
library(lares)
library(DMwR)
library(gridExtra)
library(rattle)
library(RColorBrewer)
library(stats)
library(caretEnsemble)
library(pROC)
library(adabag)
library(gbm)
library(xgboost)
##################################
# Loading source and
# formulating the analysis set
##################################
BreastCancer <- read.csv("WisconsinBreastCancer.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
BreastCancer <- as.data.frame(BreastCancer)
##################################
# Performing a general exploration of the data set
##################################
dim(BreastCancer)## [1] 1138 32str(BreastCancer)## 'data.frame': 1138 obs. of 32 variables:
## $ id : int 842302 842517 84300903 84348301 84358402 843786 844359 84458202 844981 84501001 ...
## $ diagnosis : chr "M" "M" "M" "M" ...
## $ radius_mean : num 18 20.6 19.7 11.4 20.3 ...
## $ texture_mean : num 10.4 17.8 21.2 20.4 14.3 ...
## $ perimeter_mean : num 122.8 132.9 130 77.6 135.1 ...
## $ area_mean : num 1001 1326 1203 386 1297 ...
## $ smoothness_mean : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## $ compactness_mean : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## $ concavity_mean : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
## $ concave.points_mean : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## $ symmetry_mean : num 0.242 0.181 0.207 0.26 0.181 ...
## $ fractal_dimension_mean : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
## $ radius_se : num 1.095 0.543 0.746 0.496 0.757 ...
## $ texture_se : num 0.905 0.734 0.787 1.156 0.781 ...
## $ perimeter_se : num 8.59 3.4 4.58 3.44 5.44 ...
## $ area_se : num 153.4 74.1 94 27.2 94.4 ...
## $ smoothness_se : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## $ compactness_se : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
## $ concavity_se : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## $ concave.points_se : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## $ symmetry_se : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
## $ fractal_dimension_se : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## $ radius_worst : num 25.4 25 23.6 14.9 22.5 ...
## $ texture_worst : num 17.3 23.4 25.5 26.5 16.7 ...
## $ perimeter_worst : num 184.6 158.8 152.5 98.9 152.2 ...
## $ area_worst : num 2019 1956 1709 568 1575 ...
## $ smoothness_worst : num 0.162 0.124 0.144 0.21 0.137 ...
## $ compactness_worst : num 0.666 0.187 0.424 0.866 0.205 ...
## $ concavity_worst : num 0.712 0.242 0.45 0.687 0.4 ...
## $ concave.points_worst : num 0.265 0.186 0.243 0.258 0.163 ...
## $ symmetry_worst : num 0.46 0.275 0.361 0.664 0.236 ...
## $ fractal_dimension_worst: num 0.1189 0.089 0.0876 0.173 0.0768 ...summary(BreastCancer)## id diagnosis radius_mean texture_mean
## Min. : 8670 Length:1138 Min. : 6.981 Min. : 9.71
## 1st Qu.: 869218 Class :character 1st Qu.:11.700 1st Qu.:16.17
## Median : 906024 Mode :character Median :13.370 Median :18.84
## Mean : 30371831 Mean :14.127 Mean :19.29
## 3rd Qu.: 8813129 3rd Qu.:15.780 3rd Qu.:21.80
## Max. :911320502 Max. :28.110 Max. :39.28
## perimeter_mean area_mean smoothness_mean compactness_mean
## Min. : 43.79 Min. : 143.5 Min. :0.05263 Min. :0.01938
## 1st Qu.: 75.17 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492
## Median : 86.24 Median : 551.1 Median :0.09587 Median :0.09263
## Mean : 91.97 Mean : 654.9 Mean :0.09636 Mean :0.10434
## 3rd Qu.:104.10 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040
## Max. :188.50 Max. :2501.0 Max. :0.16340 Max. :0.34540
## concavity_mean concave.points_mean symmetry_mean fractal_dimension_mean
## Min. :0.00000 Min. :0.00000 Min. :0.1060 Min. :0.04996
## 1st Qu.:0.02956 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770
## Median :0.06154 Median :0.03350 Median :0.1792 Median :0.06154
## Mean :0.08880 Mean :0.04892 Mean :0.1812 Mean :0.06280
## 3rd Qu.:0.13070 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612
## Max. :0.42680 Max. :0.20120 Max. :0.3040 Max. :0.09744
## radius_se texture_se perimeter_se area_se
## Min. :0.1115 Min. :0.3602 Min. : 0.757 Min. : 6.802
## 1st Qu.:0.2324 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850
## Median :0.3242 Median :1.1080 Median : 2.287 Median : 24.530
## Mean :0.4052 Mean :1.2169 Mean : 2.866 Mean : 40.337
## 3rd Qu.:0.4789 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190
## Max. :2.8730 Max. :4.8850 Max. :21.980 Max. :542.200
## smoothness_se compactness_se concavity_se concave.points_se
## Min. :0.001713 Min. :0.002252 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.005169 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638
## Median :0.006380 Median :0.020450 Median :0.02589 Median :0.010930
## Mean :0.007041 Mean :0.025478 Mean :0.03189 Mean :0.011796
## 3rd Qu.:0.008146 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710
## Max. :0.031130 Max. :0.135400 Max. :0.39600 Max. :0.052790
## symmetry_se fractal_dimension_se radius_worst texture_worst
## Min. :0.007882 Min. :0.0008948 Min. : 7.93 Min. :12.02
## 1st Qu.:0.015160 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08
## Median :0.018730 Median :0.0031870 Median :14.97 Median :25.41
## Mean :0.020542 Mean :0.0037949 Mean :16.27 Mean :25.68
## 3rd Qu.:0.023480 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72
## Max. :0.078950 Max. :0.0298400 Max. :36.04 Max. :49.54
## perimeter_worst area_worst smoothness_worst compactness_worst
## Min. : 50.41 Min. : 185.2 Min. :0.07117 Min. :0.02729
## 1st Qu.: 84.11 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720
## Median : 97.66 Median : 686.5 Median :0.13130 Median :0.21190
## Mean :107.26 Mean : 880.6 Mean :0.13237 Mean :0.25427
## 3rd Qu.:125.40 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910
## Max. :251.20 Max. :4254.0 Max. :0.22260 Max. :1.05800
## concavity_worst concave.points_worst symmetry_worst fractal_dimension_worst
## Min. :0.0000 Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.1145 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.2267 Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.2722 Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.3829 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :1.2520 Max. :0.29100 Max. :0.6638 Max. :0.20750##################################
# Setting the data type
# for the response variable
##################################
BreastCancer$diagnosis <- factor(BreastCancer$diagnosis,
levels = c("M","B"))
##################################
# Formulating a data type assessment summary
##################################
PDA <- BreastCancer
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 id integer
## 2 2 diagnosis factor
## 3 3 radius_mean numeric
## 4 4 texture_mean numeric
## 5 5 perimeter_mean numeric
## 6 6 area_mean numeric
## 7 7 smoothness_mean numeric
## 8 8 compactness_mean numeric
## 9 9 concavity_mean numeric
## 10 10 concave.points_mean numeric
## 11 11 symmetry_mean numeric
## 12 12 fractal_dimension_mean numeric
## 13 13 radius_se numeric
## 14 14 texture_se numeric
## 15 15 perimeter_se numeric
## 16 16 area_se numeric
## 17 17 smoothness_se numeric
## 18 18 compactness_se numeric
## 19 19 concavity_se numeric
## 20 20 concave.points_se numeric
## 21 21 symmetry_se numeric
## 22 22 fractal_dimension_se numeric
## 23 23 radius_worst numeric
## 24 24 texture_worst numeric
## 25 25 perimeter_worst numeric
## 26 26 area_worst numeric
## 27 27 smoothness_worst numeric
## 28 28 compactness_worst numeric
## 29 29 concavity_worst numeric
## 30 30 concave.points_worst numeric
## 31 31 symmetry_worst numeric
## 32 32 fractal_dimension_worst numeric1.2 Data Quality Assessment
[A] No missing observations noted for any predictor.
[B] Low variance observed for 1 predictor with First.Second.Mode.Ratio>5.
[B.1] concavity_se = 6.50
[C] No low variance observed for any predictor with Unique.Count.Ratio<0.01.
[D] High skewness observed for 5 predictors with Skewness>3 or Skewness<(-3).
[D.1] radius_se = +3.08
[D.2] perimeter_se = +3.43
[D.3] area_se = +5.43
[D.4] concavity_se = +5.10
[D.5] fractal_dimension_se = +3.91
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- BreastCancer
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 id integer 1138 0 1.000
## 2 diagnosis factor 1138 0 1.000
## 3 radius_mean numeric 1138 0 1.000
## 4 texture_mean numeric 1138 0 1.000
## 5 perimeter_mean numeric 1138 0 1.000
## 6 area_mean numeric 1138 0 1.000
## 7 smoothness_mean numeric 1138 0 1.000
## 8 compactness_mean numeric 1138 0 1.000
## 9 concavity_mean numeric 1138 0 1.000
## 10 concave.points_mean numeric 1138 0 1.000
## 11 symmetry_mean numeric 1138 0 1.000
## 12 fractal_dimension_mean numeric 1138 0 1.000
## 13 radius_se numeric 1138 0 1.000
## 14 texture_se numeric 1138 0 1.000
## 15 perimeter_se numeric 1138 0 1.000
## 16 area_se numeric 1138 0 1.000
## 17 smoothness_se numeric 1138 0 1.000
## 18 compactness_se numeric 1138 0 1.000
## 19 concavity_se numeric 1138 0 1.000
## 20 concave.points_se numeric 1138 0 1.000
## 21 symmetry_se numeric 1138 0 1.000
## 22 fractal_dimension_se numeric 1138 0 1.000
## 23 radius_worst numeric 1138 0 1.000
## 24 texture_worst numeric 1138 0 1.000
## 25 perimeter_worst numeric 1138 0 1.000
## 26 area_worst numeric 1138 0 1.000
## 27 smoothness_worst numeric 1138 0 1.000
## 28 compactness_worst numeric 1138 0 1.000
## 29 concavity_worst numeric 1138 0 1.000
## 30 concave.points_worst numeric 1138 0 1.000
## 31 symmetry_worst numeric 1138 0 1.000
## 32 fractal_dimension_worst numeric 1138 0 1.000##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("id","diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric)]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 30 numeric predictor variable(s)."##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor)]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are no factor predictor variables."##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 1 radius_mean numeric 456 0.401
## 2 texture_mean numeric 479 0.421
## 3 perimeter_mean numeric 522 0.459
## 4 area_mean numeric 539 0.474
## 5 smoothness_mean numeric 474 0.417
## 6 compactness_mean numeric 537 0.472
## 7 concavity_mean numeric 537 0.472
## 8 concave.points_mean numeric 542 0.476
## 9 symmetry_mean numeric 432 0.380
## 10 fractal_dimension_mean numeric 499 0.438
## 11 radius_se numeric 540 0.475
## 12 texture_se numeric 519 0.456
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 15 smoothness_se numeric 547 0.481
## 16 compactness_se numeric 541 0.475
## 17 concavity_se numeric 533 0.468
## 18 concave.points_se numeric 507 0.446
## 19 symmetry_se numeric 498 0.438
## 20 fractal_dimension_se numeric 545 0.479
## 21 radius_worst numeric 457 0.402
## 22 texture_worst numeric 511 0.449
## 23 perimeter_worst numeric 514 0.452
## 24 area_worst numeric 544 0.478
## 25 smoothness_worst numeric 411 0.361
## 26 compactness_worst numeric 529 0.465
## 27 concavity_worst numeric 539 0.474
## 28 concave.points_worst numeric 492 0.432
## 29 symmetry_worst numeric 500 0.439
## 30 fractal_dimension_worst numeric 535 0.470
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 1 12.340 13.000 8 6
## 2 15.700 21.250 6 4
## 3 82.610 132.900 6 4
## 4 512.200 658.800 6 4
## 5 0.101 0.108 10 8
## 6 0.121 0.160 6 4
## 7 0.000 0.120 26 6
## 8 0.000 0.029 26 6
## 9 0.177 0.181 8 6
## 10 0.057 0.059 6 4
## 11 0.286 0.298 6 4
## 12 1.150 0.734 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 15 0.006 0.005 4 2
## 16 0.023 0.014 6 4
## 17 0.000 0.017 26 4
## 18 0.000 0.012 26 6
## 19 0.013 0.015 8 6
## 20 0.003 0.006 4 2
## 21 12.360 13.340 10 8
## 22 27.260 27.660 6 4
## 23 117.700 184.600 6 4
## 24 1269.000 2019.000 4 2
## 25 0.131 0.149 8 6
## 26 0.342 0.177 6 4
## 27 0.000 0.450 26 6
## 28 0.000 0.026 26 6
## 29 0.320 0.361 6 4
## 30 0.074 0.084 6 4
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 1 1.333 6.981 14.127 13.370 28.110 0.940 3.828
## 2 1.500 9.710 19.290 18.840 39.280 0.649 3.741
## 3 1.500 43.790 91.969 86.240 188.500 0.988 3.953
## 4 1.500 143.500 654.889 551.100 2501.000 1.641 6.610
## 5 1.250 0.053 0.096 0.096 0.163 0.455 3.838
## 6 1.500 0.019 0.104 0.093 0.345 1.187 4.625
## 7 4.333 0.000 0.089 0.062 0.427 1.397 4.971
## 8 4.333 0.000 0.049 0.034 0.201 1.168 4.047
## 9 1.333 0.106 0.181 0.179 0.304 0.724 4.266
## 10 1.500 0.050 0.063 0.062 0.097 1.301 5.969
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 12 1.500 0.360 1.217 1.108 4.885 1.642 8.292
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 15 2.000 0.002 0.007 0.006 0.031 2.308 13.368
## 16 1.500 0.002 0.025 0.020 0.135 1.897 8.051
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 18 4.333 0.000 0.012 0.011 0.053 1.441 8.071
## 19 1.333 0.008 0.021 0.019 0.079 2.189 10.816
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## 21 1.250 7.930 16.269 14.970 36.040 1.100 3.925
## 22 1.500 12.020 25.677 25.410 49.540 0.497 3.212
## 23 1.500 50.410 107.261 97.660 251.200 1.125 4.050
## 24 2.000 185.200 880.583 686.500 4254.000 1.854 7.347
## 25 1.333 0.071 0.132 0.131 0.223 0.414 3.503
## 26 1.500 0.027 0.254 0.212 1.058 1.470 6.002
## 27 4.333 0.000 0.272 0.227 1.252 1.147 4.591
## 28 4.333 0.000 0.115 0.100 0.291 0.491 2.459
## 29 1.500 0.156 0.290 0.282 0.664 1.430 7.395
## 30 1.500 0.055 0.084 0.080 0.208 1.658 8.188
## Percentile25th Percentile75th
## 1 11.700 15.780
## 2 16.170 21.800
## 3 75.170 104.100
## 4 420.300 782.700
## 5 0.086 0.105
## 6 0.065 0.130
## 7 0.030 0.131
## 8 0.020 0.074
## 9 0.162 0.196
## 10 0.058 0.066
## 11 0.232 0.479
## 12 0.834 1.474
## 13 1.606 3.357
## 14 17.850 45.190
## 15 0.005 0.008
## 16 0.013 0.032
## 17 0.015 0.042
## 18 0.008 0.015
## 19 0.015 0.023
## 20 0.002 0.005
## 21 13.010 18.790
## 22 21.080 29.720
## 23 84.110 125.400
## 24 515.300 1084.000
## 25 0.117 0.146
## 26 0.147 0.339
## 27 0.114 0.383
## 28 0.065 0.161
## 29 0.250 0.318
## 30 0.071 0.092##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "No factor predictors noted."if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 17 concavity_se numeric 533 0.468 0.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 17 0.017 26 4 6.500
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 17 0.000 0.032 0.026 0.396 5.097 51.423 0.015 0.042if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric predictors due to low unique count ratio noted."##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric predictors noted.")
}## [1] "High skewness observed for 5 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 11 radius_se numeric 540 0.475
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 17 concavity_se numeric 533 0.468
## 20 fractal_dimension_se numeric 545 0.479
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 11 0.286 0.298 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 17 0.000 0.017 26 4
## 20 0.003 0.006 4 2
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## Percentile25th Percentile75th
## 11 0.232 0.479
## 13 1.606 3.357
## 14 17.850 45.190
## 17 0.015 0.042
## 20 0.002 0.0051.3 Data Preprocessing
1.3.1 Outlier Detection
[A] Outliers noted for 29 out of the 30 predictors. Predictor values were visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile).
[A.1] radius_mean = 28
[A.2] texture_mean = 14
[A.3] perimeter_mean = 26
[A.4] area_mean = 50
[A.5] smoothness_mean = 12
[A.6] compactness_mean = 32
[A.7] concavity_mean = 36
[A.8] concave.points_mean = 20
[A.9] symmetry_mean = 30
[A.10] fractal_dimension_mean = 30
[A.11] radius_se = 76
[A.12] texture_se = 40
[A.13] perimeter_se = 76
[A.14] area_se = 130
[A.15] smoothness_se = 60
[A.16] compactness_se = 56
[A.17] concavity_se = 44
[A.18] concave.points_se = 38
[A.19] symmetry_se = 54
[A.20] fractal_dimension_se = 56
[A.21] radius_worst = 34
[A.22] texture_worst = 10
[A.23] perimeter_worst = 30
[A.24] area_worst = 70
[A.25] smoothness_worst = 14
[A.26] compactness_worst = 32
[A.27] concavity_worst = 24
[A.28] symmetry_worst = 46
[A.29] fractal_dimension_worst = 48
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- DQA[,!names(DQA) %in% c("id")]
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA)) | Name | DPA |
| Number of rows | 1138 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 30 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| radius_mean | 0 | 1 | 14.13 | 3.52 | 6.98 | 11.70 | 13.37 | 15.78 | 28.11 | ▂▇▃▁▁ |
| texture_mean | 0 | 1 | 19.29 | 4.30 | 9.71 | 16.17 | 18.84 | 21.80 | 39.28 | ▃▇▃▁▁ |
| perimeter_mean | 0 | 1 | 91.97 | 24.29 | 43.79 | 75.17 | 86.24 | 104.10 | 188.50 | ▃▇▃▁▁ |
| area_mean | 0 | 1 | 654.89 | 351.76 | 143.50 | 420.30 | 551.10 | 782.70 | 2501.00 | ▇▃▂▁▁ |
| smoothness_mean | 0 | 1 | 0.10 | 0.01 | 0.05 | 0.09 | 0.10 | 0.11 | 0.16 | ▁▇▇▁▁ |
| compactness_mean | 0 | 1 | 0.10 | 0.05 | 0.02 | 0.06 | 0.09 | 0.13 | 0.35 | ▇▇▂▁▁ |
| concavity_mean | 0 | 1 | 0.09 | 0.08 | 0.00 | 0.03 | 0.06 | 0.13 | 0.43 | ▇▃▂▁▁ |
| concave.points_mean | 0 | 1 | 0.05 | 0.04 | 0.00 | 0.02 | 0.03 | 0.07 | 0.20 | ▇▃▂▁▁ |
| symmetry_mean | 0 | 1 | 0.18 | 0.03 | 0.11 | 0.16 | 0.18 | 0.20 | 0.30 | ▁▇▅▁▁ |
| fractal_dimension_mean | 0 | 1 | 0.06 | 0.01 | 0.05 | 0.06 | 0.06 | 0.07 | 0.10 | ▆▇▂▁▁ |
| radius_se | 0 | 1 | 0.41 | 0.28 | 0.11 | 0.23 | 0.32 | 0.48 | 2.87 | ▇▁▁▁▁ |
| texture_se | 0 | 1 | 1.22 | 0.55 | 0.36 | 0.83 | 1.11 | 1.47 | 4.88 | ▇▅▁▁▁ |
| perimeter_se | 0 | 1 | 2.87 | 2.02 | 0.76 | 1.61 | 2.29 | 3.36 | 21.98 | ▇▁▁▁▁ |
| area_se | 0 | 1 | 40.34 | 45.47 | 6.80 | 17.85 | 24.53 | 45.19 | 542.20 | ▇▁▁▁▁ |
| smoothness_se | 0 | 1 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.03 | ▇▃▁▁▁ |
| compactness_se | 0 | 1 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.03 | 0.14 | ▇▃▁▁▁ |
| concavity_se | 0 | 1 | 0.03 | 0.03 | 0.00 | 0.02 | 0.03 | 0.04 | 0.40 | ▇▁▁▁▁ |
| concave.points_se | 0 | 1 | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | 0.05 | ▇▇▁▁▁ |
| symmetry_se | 0 | 1 | 0.02 | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | 0.08 | ▇▃▁▁▁ |
| fractal_dimension_se | 0 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | ▇▁▁▁▁ |
| radius_worst | 0 | 1 | 16.27 | 4.83 | 7.93 | 13.01 | 14.97 | 18.79 | 36.04 | ▆▇▃▁▁ |
| texture_worst | 0 | 1 | 25.68 | 6.14 | 12.02 | 21.08 | 25.41 | 29.72 | 49.54 | ▃▇▆▁▁ |
| perimeter_worst | 0 | 1 | 107.26 | 33.59 | 50.41 | 84.11 | 97.66 | 125.40 | 251.20 | ▇▇▃▁▁ |
| area_worst | 0 | 1 | 880.58 | 569.11 | 185.20 | 515.30 | 686.50 | 1084.00 | 4254.00 | ▇▂▁▁▁ |
| smoothness_worst | 0 | 1 | 0.13 | 0.02 | 0.07 | 0.12 | 0.13 | 0.15 | 0.22 | ▂▇▇▂▁ |
| compactness_worst | 0 | 1 | 0.25 | 0.16 | 0.03 | 0.15 | 0.21 | 0.34 | 1.06 | ▇▅▁▁▁ |
| concavity_worst | 0 | 1 | 0.27 | 0.21 | 0.00 | 0.11 | 0.23 | 0.38 | 1.25 | ▇▅▂▁▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | 0.29 | 0.06 | 0.16 | 0.25 | 0.28 | 0.32 | 0.66 | ▅▇▁▁▁ |
| fractal_dimension_worst | 0 | 1 | 0.08 | 0.02 | 0.06 | 0.07 | 0.08 | 0.09 | 0.21 | ▇▃▁▁▁ |
##################################
# Outlier Detection
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric Predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
1.3.2 Zero and Near-Zero Variance
[A] No low variance observed for any predictor using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## [1] freqRatio percentUnique zeroVar nzv
## <0 rows> (or 0-length row.names)if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance descriptors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "No low variance descriptors noted."1.3.3 Collinearity
[A] High correlation values were noted for 15 pairs of numeric predictors with Pearson correlation coefficients >80% as confirmed using the preprocessing summaries from the caret package.
[A.1] radius_mean and perimeter_mean = +100%
[A.2] radius_worst and perimeter_worst = +99%
[A.3] radius_mean and area_mean = +99%
[A.4] perimeter_mean and area_mean = +99%
[A.5] radius_worst and area_worst = +98%
[A.6] perimeter_worst and area_worst = +98%
[A.7] radius_se and perimeter_se = +97%
[A.8] perimeter_mean and perimeter_worst = +97%
[A.9] radius_mean and radius_worst = +97%
[A.10] perimeter_mean and radius_worst = +97%%
[A.11] radius_mean and perimeter_worst = +96%
[A.12] area_mean and radius_worst = +96%
[A.13] area_mean and area_worst = +96%
[A.14] area_mean and perimeter_worst = +96%
[A.15] radius_se and area_se = +95%
[B] 7 predictors driving high pairwise correlation were recommended for removal using the findCorrelation preprocessing method from the caret package. The function looks at the mean absolute correlation of each predictor and removes that with the largest mean absolute correlation.
[B.1] perimeter_worst
[B.2] radius_worst
[B.3] perimeter_mean
[B.4] area_worst
[B.5] radius_mean
[B.6] perimeter_se
[B.7] area_se
Code Chunk | Output
##################################
# Visualizing pairwise correlation between Predictor
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## radius_mean texture_mean perimeter_mean area_mean
## radius_mean 1.000000000 0.323781891 0.997855281 0.987357170
## texture_mean 0.323781891 1.000000000 0.329533059 0.321085696
## perimeter_mean 0.997855281 0.329533059 1.000000000 0.986506804
## area_mean 0.987357170 0.321085696 0.986506804 1.000000000
## smoothness_mean 0.170581187 -0.023388516 0.207278164 0.177028377
## compactness_mean 0.506123578 0.236702222 0.556936211 0.498501682
## concavity_mean 0.676763550 0.302417828 0.716135650 0.685982829
## concave.points_mean 0.822528522 0.293464051 0.850977041 0.823268869
## symmetry_mean 0.147741242 0.071400980 0.183027212 0.151293079
## fractal_dimension_mean -0.311630826 -0.076437183 -0.261476908 -0.283109812
## radius_se 0.679090388 0.275868676 0.691765014 0.732562227
## texture_se -0.097317443 0.386357623 -0.086761078 -0.066280214
## perimeter_se 0.674171616 0.281673115 0.693134890 0.726628328
## area_se 0.735863663 0.259844987 0.744982694 0.800085921
## smoothness_se -0.222600125 0.006613777 -0.202694026 -0.166776667
## compactness_se 0.205999980 0.191974611 0.250743681 0.212582551
## concavity_se 0.194203623 0.143293077 0.228082345 0.207660060
## concave.points_se 0.376168956 0.163851025 0.407216916 0.372320282
## symmetry_se -0.104320881 0.009127168 -0.081629327 -0.072496588
## fractal_dimension_se -0.042641269 0.054457520 -0.005523391 -0.019886963
## radius_worst 0.969538973 0.352572947 0.969476363 0.962746086
## texture_worst 0.297007644 0.912044589 0.303038372 0.287488627
## perimeter_worst 0.965136514 0.358039575 0.970386887 0.959119574
## area_worst 0.941082460 0.343545947 0.941549808 0.959213326
## smoothness_worst 0.119616140 0.077503359 0.150549404 0.123522939
## compactness_worst 0.413462823 0.277829592 0.455774228 0.390410309
## concavity_worst 0.526911462 0.301025224 0.563879263 0.512605920
## concave.points_worst 0.744214198 0.295315843 0.771240789 0.722016626
## symmetry_worst 0.163953335 0.105007910 0.189115040 0.143569914
## fractal_dimension_worst 0.007065886 0.119205351 0.051018530 0.003737597
## smoothness_mean compactness_mean concavity_mean
## radius_mean 0.17058119 0.50612358 0.67676355
## texture_mean -0.02338852 0.23670222 0.30241783
## perimeter_mean 0.20727816 0.55693621 0.71613565
## area_mean 0.17702838 0.49850168 0.68598283
## smoothness_mean 1.00000000 0.65912322 0.52198377
## compactness_mean 0.65912322 1.00000000 0.88312067
## concavity_mean 0.52198377 0.88312067 1.00000000
## concave.points_mean 0.55369517 0.83113504 0.92139103
## symmetry_mean 0.55777479 0.60264105 0.50066662
## fractal_dimension_mean 0.58479200 0.56536866 0.33678336
## radius_se 0.30146710 0.49747345 0.63192482
## texture_se 0.06840645 0.04620483 0.07621835
## perimeter_se 0.29609193 0.54890526 0.66039079
## area_se 0.24655243 0.45565285 0.61742681
## smoothness_se 0.33237544 0.13529927 0.09856375
## compactness_se 0.31894330 0.73872179 0.67027882
## concavity_se 0.24839568 0.57051687 0.69127021
## concave.points_se 0.38067569 0.64226185 0.68325992
## symmetry_se 0.20077438 0.22997659 0.17800921
## fractal_dimension_se 0.28360670 0.50731813 0.44930075
## radius_worst 0.21312014 0.53531540 0.68823641
## texture_worst 0.03607180 0.24813283 0.29987889
## perimeter_worst 0.23885263 0.59021043 0.72956492
## area_worst 0.20671836 0.50960381 0.67598723
## smoothness_worst 0.80532420 0.56554117 0.44882204
## compactness_worst 0.47246844 0.86580904 0.75496802
## concavity_worst 0.43492571 0.81627525 0.88410264
## concave.points_worst 0.50305335 0.81557322 0.86132303
## symmetry_worst 0.39430948 0.51022343 0.40946413
## fractal_dimension_worst 0.49931637 0.68738232 0.51492989
## concave.points_mean symmetry_mean
## radius_mean 0.82252852 0.14774124
## texture_mean 0.29346405 0.07140098
## perimeter_mean 0.85097704 0.18302721
## area_mean 0.82326887 0.15129308
## smoothness_mean 0.55369517 0.55777479
## compactness_mean 0.83113504 0.60264105
## concavity_mean 0.92139103 0.50066662
## concave.points_mean 1.00000000 0.46249739
## symmetry_mean 0.46249739 1.00000000
## fractal_dimension_mean 0.16691738 0.47992133
## radius_se 0.69804983 0.30337926
## texture_se 0.02147958 0.12805293
## perimeter_se 0.71064987 0.31389276
## area_se 0.69029854 0.22397022
## smoothness_se 0.02765331 0.18732117
## compactness_se 0.49042425 0.42165915
## concavity_se 0.43916707 0.34262702
## concave.points_se 0.61563413 0.39329787
## symmetry_se 0.09535079 0.44913654
## fractal_dimension_se 0.25758375 0.33178615
## radius_worst 0.83031763 0.18572775
## texture_worst 0.29275171 0.09065069
## perimeter_worst 0.85592313 0.21916856
## area_worst 0.80962962 0.17719338
## smoothness_worst 0.45275305 0.42667503
## compactness_worst 0.66745368 0.47320001
## concavity_worst 0.75239950 0.43372101
## concave.points_worst 0.91015531 0.43029661
## symmetry_worst 0.37574415 0.69982580
## fractal_dimension_worst 0.36866113 0.43841350
## fractal_dimension_mean radius_se texture_se
## radius_mean -0.3116308263 0.6790903880 -0.09731744
## texture_mean -0.0764371834 0.2758686762 0.38635762
## perimeter_mean -0.2614769081 0.6917650135 -0.08676108
## area_mean -0.2831098117 0.7325622270 -0.06628021
## smoothness_mean 0.5847920019 0.3014670983 0.06840645
## compactness_mean 0.5653686634 0.4974734461 0.04620483
## concavity_mean 0.3367833594 0.6319248221 0.07621835
## concave.points_mean 0.1669173832 0.6980498336 0.02147958
## symmetry_mean 0.4799213301 0.3033792632 0.12805293
## fractal_dimension_mean 1.0000000000 0.0001109951 0.16417397
## radius_se 0.0001109951 1.0000000000 0.21324734
## texture_se 0.1641739659 0.2132473373 1.00000000
## perimeter_se 0.0398299316 0.9727936770 0.22317073
## area_se -0.0901702475 0.9518301121 0.11156725
## smoothness_se 0.4019644254 0.1645142198 0.39724285
## compactness_se 0.5598366906 0.3560645755 0.23169970
## concavity_se 0.4466303217 0.3323575376 0.19499846
## concave.points_se 0.3411980444 0.5133464414 0.23028340
## symmetry_se 0.3450073971 0.2405673625 0.41162068
## fractal_dimension_se 0.6881315775 0.2277535327 0.27972275
## radius_worst -0.2536914949 0.7150651951 -0.11169031
## texture_worst -0.0512692020 0.1947985568 0.40900277
## perimeter_worst -0.2051512113 0.7196838037 -0.10224192
## area_worst -0.2318544512 0.7515484761 -0.08319499
## smoothness_worst 0.5049420754 0.1419185529 -0.07365766
## compactness_worst 0.4587981567 0.2871031656 -0.09243935
## concavity_worst 0.3462338763 0.3805846346 -0.06895622
## concave.points_worst 0.1753254492 0.5310623278 -0.11963752
## symmetry_worst 0.3340186839 0.0945428304 -0.12821476
## fractal_dimension_worst 0.7672967792 0.0495594325 -0.04565457
## perimeter_se area_se smoothness_se compactness_se
## radius_mean 0.67417162 0.73586366 -0.222600125 0.2060000
## texture_mean 0.28167311 0.25984499 0.006613777 0.1919746
## perimeter_mean 0.69313489 0.74498269 -0.202694026 0.2507437
## area_mean 0.72662833 0.80008592 -0.166776667 0.2125826
## smoothness_mean 0.29609193 0.24655243 0.332375443 0.3189433
## compactness_mean 0.54890526 0.45565285 0.135299268 0.7387218
## concavity_mean 0.66039079 0.61742681 0.098563746 0.6702788
## concave.points_mean 0.71064987 0.69029854 0.027653308 0.4904242
## symmetry_mean 0.31389276 0.22397022 0.187321165 0.4216591
## fractal_dimension_mean 0.03982993 -0.09017025 0.401964425 0.5598367
## radius_se 0.97279368 0.95183011 0.164514220 0.3560646
## texture_se 0.22317073 0.11156725 0.397242853 0.2316997
## perimeter_se 1.00000000 0.93765541 0.151075331 0.4163224
## area_se 0.93765541 1.00000000 0.075150338 0.2848401
## smoothness_se 0.15107533 0.07515034 1.000000000 0.3366961
## compactness_se 0.41632237 0.28484006 0.336696081 1.0000000
## concavity_se 0.36248158 0.27089473 0.268684760 0.8012683
## concave.points_se 0.55626408 0.41572957 0.328429499 0.7440827
## symmetry_se 0.26648709 0.13410898 0.413506125 0.3947128
## fractal_dimension_se 0.24414277 0.12707090 0.427374207 0.8032688
## radius_worst 0.69720059 0.75737319 -0.230690710 0.2046072
## texture_worst 0.20037085 0.19649665 -0.074742965 0.1430026
## perimeter_worst 0.72103131 0.76121264 -0.217303755 0.2605158
## area_worst 0.73071297 0.81140796 -0.182195478 0.1993713
## smoothness_worst 0.13005439 0.12538943 0.314457456 0.2273942
## compactness_worst 0.34191945 0.28325654 -0.055558139 0.6787804
## concavity_worst 0.41889882 0.38510014 -0.058298387 0.6391467
## concave.points_worst 0.55489723 0.53816631 -0.102006796 0.4832083
## symmetry_worst 0.10993043 0.07412629 -0.107342098 0.2778784
## fractal_dimension_worst 0.08543257 0.01753930 0.101480315 0.5909728
## concavity_se concave.points_se symmetry_se
## radius_mean 0.1942036 0.37616896 -0.104320881
## texture_mean 0.1432931 0.16385103 0.009127168
## perimeter_mean 0.2280823 0.40721692 -0.081629327
## area_mean 0.2076601 0.37232028 -0.072496588
## smoothness_mean 0.2483957 0.38067569 0.200774376
## compactness_mean 0.5705169 0.64226185 0.229976591
## concavity_mean 0.6912702 0.68325992 0.178009208
## concave.points_mean 0.4391671 0.61563413 0.095350787
## symmetry_mean 0.3426270 0.39329787 0.449136542
## fractal_dimension_mean 0.4466303 0.34119804 0.345007397
## radius_se 0.3323575 0.51334644 0.240567362
## texture_se 0.1949985 0.23028340 0.411620680
## perimeter_se 0.3624816 0.55626408 0.266487092
## area_se 0.2708947 0.41572957 0.134108980
## smoothness_se 0.2686848 0.32842950 0.413506125
## compactness_se 0.8012683 0.74408267 0.394712835
## concavity_se 1.0000000 0.77180399 0.309428578
## concave.points_se 0.7718040 1.00000000 0.312780223
## symmetry_se 0.3094286 0.31278022 1.000000000
## fractal_dimension_se 0.7273722 0.61104414 0.369078083
## radius_worst 0.1869035 0.35812667 -0.128120769
## texture_worst 0.1002410 0.08674121 -0.077473420
## perimeter_worst 0.2266804 0.39499925 -0.103753044
## area_worst 0.1883527 0.34227116 -0.110342743
## smoothness_worst 0.1684813 0.21535060 -0.012661800
## compactness_worst 0.4848578 0.45288838 0.060254879
## concavity_worst 0.6625641 0.54959238 0.037119049
## concave.points_worst 0.4404723 0.60244961 -0.030413396
## symmetry_worst 0.1977878 0.14311567 0.389402485
## fractal_dimension_worst 0.4393293 0.31065455 0.078079476
## fractal_dimension_se radius_worst texture_worst
## radius_mean -0.042641269 0.96953897 0.297007644
## texture_mean 0.054457520 0.35257295 0.912044589
## perimeter_mean -0.005523391 0.96947636 0.303038372
## area_mean -0.019886963 0.96274609 0.287488627
## smoothness_mean 0.283606699 0.21312014 0.036071799
## compactness_mean 0.507318127 0.53531540 0.248132833
## concavity_mean 0.449300749 0.68823641 0.299878889
## concave.points_mean 0.257583746 0.83031763 0.292751713
## symmetry_mean 0.331786146 0.18572775 0.090650688
## fractal_dimension_mean 0.688131577 -0.25369149 -0.051269202
## radius_se 0.227753533 0.71506520 0.194798557
## texture_se 0.279722748 -0.11169031 0.409002766
## perimeter_se 0.244142773 0.69720059 0.200370854
## area_se 0.127070903 0.75737319 0.196496649
## smoothness_se 0.427374207 -0.23069071 -0.074742965
## compactness_se 0.803268818 0.20460717 0.143002583
## concavity_se 0.727372184 0.18690352 0.100240984
## concave.points_se 0.611044139 0.35812667 0.086741210
## symmetry_se 0.369078083 -0.12812077 -0.077473420
## fractal_dimension_se 1.000000000 -0.03748762 -0.003195029
## radius_worst -0.037487618 1.00000000 0.359920754
## texture_worst -0.003195029 0.35992075 1.000000000
## perimeter_worst -0.001000398 0.99370792 0.365098245
## area_worst -0.022736147 0.98401456 0.345842283
## smoothness_worst 0.170568316 0.21657443 0.225429415
## compactness_worst 0.390158842 0.47582004 0.360832339
## concavity_worst 0.379974661 0.57397471 0.368365607
## concave.points_worst 0.215204013 0.78742385 0.359754610
## symmetry_worst 0.111093956 0.24352920 0.233027461
## fractal_dimension_worst 0.591328066 0.09349198 0.219122425
## perimeter_worst area_worst smoothness_worst
## radius_mean 0.965136514 0.94108246 0.11961614
## texture_mean 0.358039575 0.34354595 0.07750336
## perimeter_mean 0.970386887 0.94154981 0.15054940
## area_mean 0.959119574 0.95921333 0.12352294
## smoothness_mean 0.238852626 0.20671836 0.80532420
## compactness_mean 0.590210428 0.50960381 0.56554117
## concavity_mean 0.729564917 0.67598723 0.44882204
## concave.points_mean 0.855923128 0.80962962 0.45275305
## symmetry_mean 0.219168559 0.17719338 0.42667503
## fractal_dimension_mean -0.205151211 -0.23185445 0.50494208
## radius_se 0.719683804 0.75154848 0.14191855
## texture_se -0.102241922 -0.08319499 -0.07365766
## perimeter_se 0.721031310 0.73071297 0.13005439
## area_se 0.761212636 0.81140796 0.12538943
## smoothness_se -0.217303755 -0.18219548 0.31445746
## compactness_se 0.260515840 0.19937133 0.22739423
## concavity_se 0.226680426 0.18835265 0.16848132
## concave.points_se 0.394999252 0.34227116 0.21535060
## symmetry_se -0.103753044 -0.11034274 -0.01266180
## fractal_dimension_se -0.001000398 -0.02273615 0.17056832
## radius_worst 0.993707916 0.98401456 0.21657443
## texture_worst 0.365098245 0.34584228 0.22542941
## perimeter_worst 1.000000000 0.97757809 0.23677460
## area_worst 0.977578091 1.00000000 0.20914533
## smoothness_worst 0.236774604 0.20914533 1.00000000
## compactness_worst 0.529407690 0.43829628 0.56818652
## concavity_worst 0.618344080 0.54333053 0.51852329
## concave.points_worst 0.816322102 0.74741880 0.54769090
## symmetry_worst 0.269492769 0.20914551 0.49383833
## fractal_dimension_worst 0.138956862 0.07964703 0.61762419
## compactness_worst concavity_worst concave.points_worst
## radius_mean 0.41346282 0.52691146 0.7442142
## texture_mean 0.27782959 0.30102522 0.2953158
## perimeter_mean 0.45577423 0.56387926 0.7712408
## area_mean 0.39041031 0.51260592 0.7220166
## smoothness_mean 0.47246844 0.43492571 0.5030534
## compactness_mean 0.86580904 0.81627525 0.8155732
## concavity_mean 0.75496802 0.88410264 0.8613230
## concave.points_mean 0.66745368 0.75239950 0.9101553
## symmetry_mean 0.47320001 0.43372101 0.4302966
## fractal_dimension_mean 0.45879816 0.34623388 0.1753254
## radius_se 0.28710317 0.38058463 0.5310623
## texture_se -0.09243935 -0.06895622 -0.1196375
## perimeter_se 0.34191945 0.41889882 0.5548972
## area_se 0.28325654 0.38510014 0.5381663
## smoothness_se -0.05555814 -0.05829839 -0.1020068
## compactness_se 0.67878035 0.63914670 0.4832083
## concavity_se 0.48485780 0.66256413 0.4404723
## concave.points_se 0.45288838 0.54959238 0.6024496
## symmetry_se 0.06025488 0.03711905 -0.0304134
## fractal_dimension_se 0.39015884 0.37997466 0.2152040
## radius_worst 0.47582004 0.57397471 0.7874239
## texture_worst 0.36083234 0.36836561 0.3597546
## perimeter_worst 0.52940769 0.61834408 0.8163221
## area_worst 0.43829628 0.54333053 0.7474188
## smoothness_worst 0.56818652 0.51852329 0.5476909
## compactness_worst 1.00000000 0.89226090 0.8010804
## concavity_worst 0.89226090 1.00000000 0.8554339
## concave.points_worst 0.80108036 0.85543386 1.0000000
## symmetry_worst 0.61444050 0.53251973 0.5025285
## fractal_dimension_worst 0.81045486 0.68651092 0.5111141
## symmetry_worst fractal_dimension_worst
## radius_mean 0.16395333 0.007065886
## texture_mean 0.10500791 0.119205351
## perimeter_mean 0.18911504 0.051018530
## area_mean 0.14356991 0.003737597
## smoothness_mean 0.39430948 0.499316369
## compactness_mean 0.51022343 0.687382323
## concavity_mean 0.40946413 0.514929891
## concave.points_mean 0.37574415 0.368661134
## symmetry_mean 0.69982580 0.438413498
## fractal_dimension_mean 0.33401868 0.767296779
## radius_se 0.09454283 0.049559432
## texture_se -0.12821476 -0.045654569
## perimeter_se 0.10993043 0.085432572
## area_se 0.07412629 0.017539295
## smoothness_se -0.10734210 0.101480315
## compactness_se 0.27787843 0.590972763
## concavity_se 0.19778782 0.439329269
## concave.points_se 0.14311567 0.310654551
## symmetry_se 0.38940248 0.078079476
## fractal_dimension_se 0.11109396 0.591328066
## radius_worst 0.24352920 0.093491979
## texture_worst 0.23302746 0.219122425
## perimeter_worst 0.26949277 0.138956862
## area_worst 0.20914551 0.079647034
## smoothness_worst 0.49383833 0.617624192
## compactness_worst 0.61444050 0.810454856
## concavity_worst 0.53251973 0.686510921
## concave.points_worst 0.50252849 0.511114146
## symmetry_worst 1.00000000 0.537848206
## fractal_dimension_worst 0.53784821 1.000000000DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = 0.95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
number.cex = 0.65,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.95))## [1] 15if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 15 pairs of numeric variable(s) with Correlation.Coefficient>0.95."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 7 numeric variable(s)."
## [1] "Variable 1 for removal: perimeter_worst"
## [1] "Variable 2 for removal: radius_worst"
## [1] "Variable 3 for removal: perimeter_mean"
## [1] "Variable 4 for removal: area_worst"
## [1] "Variable 5 for removal: radius_mean"
## [1] "Variable 6 for removal: perimeter_se"
## [1] "Variable 7 for removal: area_se"1.3.4 Linear Dependency
[A] No linear dependencies noted for any subset of numeric variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
Code Chunk | Output
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}1.3.5 Distributional Shape
[A] Shape transformation was applied to improve against skewness and minimize outliers for data distribution stability using the BoxCox method from the caret package which transforms the distributional shape for predictors with strictly positive values.
[B] Skewness measurements were improved for most except for 1 predictor with Skewness>3.
[B.1] concavity_se = +5.10
[C] Outliers were minimized for most except for 5 predictors which did not show any improvement even after shape transformation as noted using the IQR criterion.
[C.1] concavity_mean = 36
[C.2] concave.points_mean = 20
[C.3] concavity_se = 44
[C.4] concave.points_se = 38
[C.5] concavity_worst = 24
Code Chunk | Output
##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Outliers <- boxplot.stats(DPA_BoxCoxTransformed[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA_BoxCoxTransformed[,i] %in% c(Outliers))
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
DPA_BoxCoxTransformed$diagnosis <- DPA[,c("diagnosis")]1.3.6 Pre-Processed Dataset
[A] A total of 12 predictors were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[A.1] concavity_se = Low variance and high skewness
[A.2] perimeter_worst = High correlation with radius_worst, area_worst, perimeter_mean, radius_mean and area_mean
[A.3] radius_worst = High correlation with perimeter_worst, area_worst, radius_mean, perimeter_mean and area_mean
[A.4] perimeter_mean = High correlation with radius_mean, area_mean, perimeter_worst and radius_worst
[A.5] area_worst = High correlation with radius_worst, perimeter_worst and area_mean
[A.6] radius_mean = High correlation with perimeter_mean, area_mean, radius_worst and perimeter_worst .
[A.7] perimeter_se = High correlation with radius_se
[A.8] area_se = High correlation with radius_se
[A.9] concavity_mean = High outlier count even after shape transformation
[A.10] concave.points_mean = High outlier count even after shape transformation
[A.11] concave.points_se = High outlier count even after shape transformation
[A.12] concavity_worst = High outlier count even after shape transformation
[B] The preprocessed tabular dataset was comprised of 1138 observations and 19 variables (including 1 response and 18 predictors).
[B.1] 1138 rows (observations)
[B.2] 19 columns (variables)
[B.2.1] 1/19 response = diagnosis (factor)
[B.2.2] 18/19 predictors = 18/18 numeric
[B.2.2.1] texture_mean (numeric)
[B.2.2.2] area_mean (numeric)
[B.2.2.3] smoothness_mean (numeric)
[B.2.2.4] compactness_mean (numeric)
[B.2.2.5] symmetry_mean (numeric)
[B.2.2.6] fractal_dimension_mean (numeric)
[B.2.2.7] radius_se (numeric)
[B.2.2.8] texture_se (numeric)
[B.2.2.9] smoothness_se (numeric)
[B.2.2.10] compactness_se (numeric)
[B.2.2.11] symmetry_se (numeric)
[B.2.2.12] fractal_dimension_se (numeric)
[B.2.2.13] texture_worst (numeric)
[B.2.2.14] smoothness_worst (numeric)
[B.2.2.15] compactness_worst (numeric)
[B.2.2.16] concave.points_worst (numeric)
[B.2.2.17] symmetry_worst (numeric)
[B.2.2.18] fractal_dimension_worst (numeric)
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("concavity_se",
"perimeter_worst",
"radius_worst",
"perimeter_mean",
"area_worst",
"radius_mean",
"perimeter_se",
"area_se",
"concavity_mean",
"concave.points_mean",
"concave.points_se",
"concavity_worst")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 1138 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| texture_mean | 0 | 1 | 2.94 | 0.22 | 2.27 | 2.78 | 2.94 | 3.08 | 3.67 | ▁▅▇▃▁ |
| area_mean | 0 | 1 | 6.36 | 0.48 | 4.97 | 6.04 | 6.31 | 6.66 | 7.82 | ▁▅▇▃▁ |
| smoothness_mean | 0 | 1 | -2.35 | 0.15 | -2.94 | -2.45 | -2.34 | -2.25 | -1.81 | ▁▂▇▃▁ |
| compactness_mean | 0 | 1 | -2.38 | 0.49 | -3.94 | -2.73 | -2.38 | -2.04 | -1.06 | ▁▅▇▇▂ |
| symmetry_mean | 0 | 1 | -2.26 | 0.25 | -3.20 | -2.42 | -2.25 | -2.10 | -1.43 | ▁▂▇▅▁ |
| fractal_dimension_mean | 0 | 1 | -130.58 | 26.03 | -199.82 | -149.68 | -131.52 | -113.87 | -52.16 | ▁▆▇▃▁ |
| radius_se | 0 | 1 | -1.42 | 0.81 | -3.51 | -1.98 | -1.42 | -0.86 | 0.86 | ▁▆▇▅▁ |
| texture_se | 0 | 1 | 0.10 | 0.43 | -1.02 | -0.18 | 0.10 | 0.39 | 1.59 | ▂▆▇▂▁ |
| smoothness_se | 0 | 1 | -11.83 | 1.66 | -19.20 | -12.84 | -11.85 | -10.78 | -6.11 | ▁▂▇▅▁ |
| compactness_se | 0 | 1 | -3.88 | 0.65 | -6.10 | -4.34 | -3.89 | -3.43 | -2.00 | ▁▃▇▆▁ |
| symmetry_se | 0 | 1 | -16.51 | 3.52 | -28.80 | -18.91 | -16.46 | -14.16 | -5.98 | ▁▃▇▅▁ |
| fractal_dimension_se | 0 | 1 | -15.48 | 2.88 | -24.04 | -17.43 | -15.37 | -13.46 | -6.23 | ▁▅▇▃▁ |
| texture_worst | 0 | 1 | 4.53 | 0.46 | 3.22 | 4.20 | 4.55 | 4.85 | 5.91 | ▁▅▇▅▁ |
| smoothness_worst | 0 | 1 | -1.52 | 0.09 | -1.82 | -1.58 | -1.52 | -1.46 | -1.21 | ▁▃▇▃▁ |
| compactness_worst | 0 | 1 | -1.55 | 0.62 | -3.60 | -1.92 | -1.55 | -1.08 | 0.06 | ▁▃▇▆▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | -1.77 | 0.37 | -3.06 | -2.00 | -1.76 | -1.55 | -0.45 | ▁▃▇▂▁ |
| fractal_dimension_worst | 0 | 1 | -19.62 | 4.79 | -32.59 | -22.99 | -19.73 | -16.32 | -5.17 | ▁▅▇▃▁ |
1.4 Data Exploration
[A] Individual predictors which demonstrated excellent discrimination between diagnosis=M and diagnosis=B in terms of the area under the receiver operating characteristics curve (AUROC>0.80) are as follows:
[A.1] concave.points_worst = 0.97
[A.2] area_mean = 0.94
[A.3] radius_se = 0.87
[A.4] compactness_mean = 0.86
[A.5] compactness_worst = 0.86
[B] To allow a better comparison of the ensemble methods, only predictors which demonstrated fair discrimination between diagnosis=M and diagnosis=B in terms of the area under the receiver operating characteristics curve (0.70<AUROC<0.80) were selected to proceed with the modelling process, enumerated as follows:
[B.1] texture_worst = 0.78
[B.2] texture_mean = 0.77
[B.3] smoothness_worst = 0.75
[B.4] symmetry_worst = 0.74
[B.5] compactness_se = 0.73
[B.6] smoothness_mean = 0.72
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- PMA
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
ncol(DPA.Predictors.Numeric)## [1] 18##################################
# Converting response variable data type to factor
##################################
DPA$diagnosis <- as.factor(DPA$diagnosis)
length(levels(DPA$diagnosis))## [1] 2##################################
# Formulating the box plots
##################################
featurePlot(x = DPA.Predictors.Numeric,
y = DPA$diagnosis,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|",
layout = c(6, 3))
##################################
# Obtaining the AUROC
##################################
AUROC <- filterVarImp(x = DPA.Predictors.Numeric,
y = DPA$diagnosis)
##################################
# Formulating the summary table
##################################
AUROC_Summary <- AUROC
AUROC_Summary$Predictor <- rownames(AUROC)
names(AUROC_Summary)[1] <- "AUROC"
AUROC_Summary$Metric <- rep("AUROC",nrow(AUROC))
AUROC_Summary[order(AUROC_Summary$AUROC, decreasing=TRUE),] ## AUROC B Predictor Metric
## concave.points_worst 0.9667037 0.9667037 concave.points_worst AUROC
## area_mean 0.9383159 0.9383159 area_mean AUROC
## radius_se 0.8683341 0.8683341 radius_se AUROC
## compactness_mean 0.8637823 0.8637823 compactness_mean AUROC
## compactness_worst 0.8623025 0.8623025 compactness_worst AUROC
## texture_worst 0.7846308 0.7846308 texture_worst AUROC
## texture_mean 0.7758245 0.7758245 texture_mean AUROC
## smoothness_worst 0.7540563 0.7540563 smoothness_worst AUROC
## symmetry_worst 0.7369391 0.7369391 symmetry_worst AUROC
## compactness_se 0.7272805 0.7272805 compactness_se AUROC
## smoothness_mean 0.7220416 0.7220416 smoothness_mean AUROC
## symmetry_mean 0.6985624 0.6985624 symmetry_mean AUROC
## fractal_dimension_worst 0.6859706 0.6859706 fractal_dimension_worst AUROC
## fractal_dimension_se 0.6203028 0.6203028 fractal_dimension_se AUROC
## symmetry_se 0.5551107 0.5551107 symmetry_se AUROC
## smoothness_se 0.5311625 0.5311625 smoothness_se AUROC
## fractal_dimension_mean 0.5154656 0.5154656 fractal_dimension_mean AUROC
## texture_se 0.5115943 0.5115943 texture_se AUROC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ AUROC | Metric,
AUROC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
##################################
# Creating the pre-modelling dataset
# into the train and test sets
##################################
DPA <- DPA[,colnames(DPA) %in% c("diagnosis",
"texture_worst",
"texture_mean",
"smoothness_worst",
"symmetry_worst",
"compactness_se",
"smoothness_mean")]
set.seed(12345678)
MA_Train_Index <- createDataPartition(DPA$diagnosis,p=0.8)[[1]]
MA_Train <- DPA[ MA_Train_Index, ]
MA_Test <- DPA[-MA_Train_Index, ]1.5 Model Boosting
1.5.1 Adaptive Boosting (MBS_AB)
Details.
Code Chunk | Output
##################################
# Setting the cross validation process
# using the Repeated K-Fold
##################################
set.seed(12345678)
RKFold_Control <- trainControl(method="repeatedcv",
summaryFunction = twoClassSummary,
number=5,
repeats=5,
classProbs = TRUE)
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
AB_Grid = expand.grid(mfinal = c(50,100,100),
maxdepth = c(4,5,6),
coeflearn = "Breiman")
##################################
# Running the adaptive boosting model
# by setting the caret method to 'AdaBoost.M1'
##################################
set.seed(12345678)
MBS_AB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "AdaBoost.M1",
tuneGrid = AB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_AB_Tune## AdaBoost.M1
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## maxdepth mfinal ROC Sens Spec
## 4 50 0.9506186 0.8800000 0.9335561
## 4 100 0.9575073 0.8994118 0.9384409
## 5 50 0.9575898 0.8941176 0.9412265
## 5 100 0.9631176 0.8982353 0.9412265
## 6 50 0.9619147 0.8988235 0.9415927
## 6 100 0.9647554 0.9011765 0.9398352
##
## Tuning parameter 'coeflearn' was held constant at a value of Breiman
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were mfinal = 100, maxdepth = 6
## and coeflearn = Breiman.MBS_AB_Tune$finalModel## $formula
## .outcome ~ .
## <environment: 0x000000003ec71dd8>
##
## $trees
## $trees[[1]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 345 B (0.378289474 0.621710526)
## 2) smoothness_worst>=-1.500665 380 139 M (0.634210526 0.365789474)
## 4) texture_mean>=2.931727 222 34 M (0.846846847 0.153153153)
## 8) symmetry_worst>=-1.45531 64 0 M (1.000000000 0.000000000) *
## 9) symmetry_worst< -1.45531 158 34 M (0.784810127 0.215189873)
## 18) smoothness_worst< -1.437613 101 11 M (0.891089109 0.108910891)
## 36) smoothness_worst< -1.483884 32 0 M (1.000000000 0.000000000) *
## 37) smoothness_worst>=-1.483884 69 11 M (0.840579710 0.159420290)
## 74) smoothness_worst>=-1.482699 66 8 M (0.878787879 0.121212121) *
## 75) smoothness_worst< -1.482699 3 0 B (0.000000000 1.000000000) *
## 19) smoothness_worst>=-1.437613 57 23 M (0.596491228 0.403508772)
## 38) smoothness_mean>=-2.292155 42 10 M (0.761904762 0.238095238)
## 76) smoothness_mean< -2.093138 36 5 M (0.861111111 0.138888889) *
## 77) smoothness_mean>=-2.093138 6 1 B (0.166666667 0.833333333) *
## 39) smoothness_mean< -2.292155 15 2 B (0.133333333 0.866666667)
## 78) texture_mean>=3.075523 3 1 M (0.666666667 0.333333333) *
## 79) texture_mean< 3.075523 12 0 B (0.000000000 1.000000000) *
## 5) texture_mean< 2.931727 158 53 B (0.335443038 0.664556962)
## 10) compactness_se>=-3.891799 76 29 M (0.618421053 0.381578947)
## 20) symmetry_worst>=-1.668672 45 6 M (0.866666667 0.133333333)
## 40) smoothness_mean< -1.889548 43 4 M (0.906976744 0.093023256)
## 80) compactness_se< -2.646661 42 3 M (0.928571429 0.071428571) *
## 81) compactness_se>=-2.646661 1 0 B (0.000000000 1.000000000) *
## 41) smoothness_mean>=-1.889548 2 0 B (0.000000000 1.000000000) *
## 21) symmetry_worst< -1.668672 31 8 B (0.258064516 0.741935484)
## 42) compactness_se< -3.854964 5 0 M (1.000000000 0.000000000) *
## 43) compactness_se>=-3.854964 26 3 B (0.115384615 0.884615385)
## 86) smoothness_worst< -1.493233 3 1 M (0.666666667 0.333333333) *
## 87) smoothness_worst>=-1.493233 23 1 B (0.043478261 0.956521739) *
## 11) compactness_se< -3.891799 82 6 B (0.073170732 0.926829268)
## 22) smoothness_worst< -1.49885 3 0 M (1.000000000 0.000000000) *
## 23) smoothness_worst>=-1.49885 79 3 B (0.037974684 0.962025316)
## 46) texture_worst>=4.68139 3 1 M (0.666666667 0.333333333)
## 92) texture_mean>=2.892522 2 0 M (1.000000000 0.000000000) *
## 93) texture_mean< 2.892522 1 0 B (0.000000000 1.000000000) *
## 47) texture_worst< 4.68139 76 1 B (0.013157895 0.986842105)
## 94) compactness_se>=-3.970723 12 1 B (0.083333333 0.916666667) *
## 95) compactness_se< -3.970723 64 0 B (0.000000000 1.000000000) *
## 3) smoothness_worst< -1.500665 532 104 B (0.195488722 0.804511278)
## 6) texture_mean>=3.007414 171 72 B (0.421052632 0.578947368)
## 12) compactness_se>=-3.021724 22 2 M (0.909090909 0.090909091)
## 24) texture_mean>=3.038537 20 0 M (1.000000000 0.000000000) *
## 25) texture_mean< 3.038537 2 0 B (0.000000000 1.000000000) *
## 13) compactness_se< -3.021724 149 52 B (0.348993289 0.651006711)
## 26) smoothness_mean>=-2.508076 113 52 B (0.460176991 0.539823009)
## 52) symmetry_worst>=-1.527595 16 2 M (0.875000000 0.125000000)
## 104) smoothness_worst< -1.513943 14 0 M (1.000000000 0.000000000) *
## 105) smoothness_worst>=-1.513943 2 0 B (0.000000000 1.000000000) *
## 53) symmetry_worst< -1.527595 97 38 B (0.391752577 0.608247423)
## 106) smoothness_mean< -2.503847 6 0 M (1.000000000 0.000000000) *
## 107) smoothness_mean>=-2.503847 91 32 B (0.351648352 0.648351648) *
## 27) smoothness_mean< -2.508076 36 0 B (0.000000000 1.000000000) *
## 7) texture_mean< 3.007414 361 32 B (0.088642659 0.911357341)
## 14) texture_worst>=4.888103 3 0 M (1.000000000 0.000000000) *
## 15) texture_worst< 4.888103 358 29 B (0.081005587 0.918994413)
## 30) compactness_se>=-3.953942 104 18 B (0.173076923 0.826923077)
## 60) compactness_se< -3.48221 39 17 B (0.435897436 0.564102564)
## 120) compactness_se>=-3.623844 16 3 M (0.812500000 0.187500000) *
## 121) compactness_se< -3.623844 23 4 B (0.173913043 0.826086957) *
## 61) compactness_se>=-3.48221 65 1 B (0.015384615 0.984615385)
## 122) texture_mean>=2.984668 9 1 B (0.111111111 0.888888889) *
## 123) texture_mean< 2.984668 56 0 B (0.000000000 1.000000000) *
## 31) compactness_se< -3.953942 254 11 B (0.043307087 0.956692913)
## 62) texture_mean>=2.947329 28 6 B (0.214285714 0.785714286)
## 124) smoothness_worst>=-1.55438 6 1 M (0.833333333 0.166666667) *
## 125) smoothness_worst< -1.55438 22 1 B (0.045454545 0.954545455) *
## 63) texture_mean< 2.947329 226 5 B (0.022123894 0.977876106)
## 126) texture_worst>=4.389974 50 4 B (0.080000000 0.920000000) *
## 127) texture_worst< 4.389974 176 1 B (0.005681818 0.994318182) *
##
## $trees[[2]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 382 B (0.41885965 0.58114035)
## 2) texture_mean>=2.927988 495 174 M (0.64848485 0.35151515)
## 4) smoothness_mean>=-2.425205 352 76 M (0.78409091 0.21590909)
## 8) compactness_se>=-3.797621 230 26 M (0.88695652 0.11304348)
## 16) symmetry_worst>=-2.184494 209 12 M (0.94258373 0.05741627)
## 32) texture_worst>=4.35267 207 10 M (0.95169082 0.04830918)
## 64) texture_mean< 3.36829 199 7 M (0.96482412 0.03517588) *
## 65) texture_mean>=3.36829 8 3 M (0.62500000 0.37500000) *
## 33) texture_worst< 4.35267 2 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.184494 21 7 B (0.33333333 0.66666667)
## 34) symmetry_worst< -2.271177 7 0 M (1.00000000 0.00000000) *
## 35) symmetry_worst>=-2.271177 14 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.797621 122 50 M (0.59016393 0.40983607)
## 18) texture_mean>=3.057767 57 9 M (0.84210526 0.15789474)
## 36) texture_worst>=4.527762 53 5 M (0.90566038 0.09433962)
## 72) compactness_se< -3.872601 52 4 M (0.92307692 0.07692308) *
## 73) compactness_se>=-3.872601 1 0 B (0.00000000 1.00000000) *
## 37) texture_worst< 4.527762 4 0 B (0.00000000 1.00000000) *
## 19) texture_mean< 3.057767 65 24 B (0.36923077 0.63076923)
## 38) smoothness_mean< -2.309577 26 8 M (0.69230769 0.30769231)
## 76) smoothness_worst>=-1.514694 15 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst< -1.514694 11 3 B (0.27272727 0.72727273) *
## 39) smoothness_mean>=-2.309577 39 6 B (0.15384615 0.84615385)
## 78) smoothness_mean>=-2.244788 12 5 B (0.41666667 0.58333333) *
## 79) smoothness_mean< -2.244788 27 1 B (0.03703704 0.96296296) *
## 5) smoothness_mean< -2.425205 143 45 B (0.31468531 0.68531469)
## 10) symmetry_worst>=-1.695215 47 22 M (0.53191489 0.46808511)
## 20) compactness_se< -4.088469 28 6 M (0.78571429 0.21428571)
## 40) texture_mean>=2.964399 22 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.964399 6 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.088469 19 3 B (0.15789474 0.84210526)
## 42) texture_worst>=5.003123 3 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.003123 16 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.695215 96 20 B (0.20833333 0.79166667)
## 22) smoothness_worst>=-1.556752 24 9 M (0.62500000 0.37500000)
## 44) texture_mean>=3.061509 19 4 M (0.78947368 0.21052632)
## 88) compactness_se< -2.942351 16 1 M (0.93750000 0.06250000) *
## 89) compactness_se>=-2.942351 3 0 B (0.00000000 1.00000000) *
## 45) texture_mean< 3.061509 5 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.556752 72 5 B (0.06944444 0.93055556)
## 46) compactness_se>=-3.612359 20 4 B (0.20000000 0.80000000)
## 92) compactness_se< -3.580055 2 0 M (1.00000000 0.00000000) *
## 93) compactness_se>=-3.580055 18 2 B (0.11111111 0.88888889) *
## 47) compactness_se< -3.612359 52 1 B (0.01923077 0.98076923)
## 94) texture_mean< 2.966301 11 1 B (0.09090909 0.90909091) *
## 95) texture_mean>=2.966301 41 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.927988 417 61 B (0.14628297 0.85371703)
## 6) symmetry_worst>=-1.36527 29 10 M (0.65517241 0.34482759)
## 12) texture_worst>=4.373597 13 0 M (1.00000000 0.00000000) *
## 13) texture_worst< 4.373597 16 6 B (0.37500000 0.62500000)
## 26) compactness_se>=-3.446692 8 2 M (0.75000000 0.25000000)
## 52) smoothness_mean>=-2.235399 6 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.235399 2 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.446692 8 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.36527 388 42 B (0.10824742 0.89175258)
## 14) smoothness_mean>=-2.468758 288 42 B (0.14583333 0.85416667)
## 28) smoothness_mean< -2.467991 3 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.467991 285 39 B (0.13684211 0.86315789)
## 58) texture_mean>=2.656737 211 38 B (0.18009479 0.81990521)
## 116) texture_mean< 2.666527 4 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=2.666527 207 34 B (0.16425121 0.83574879) *
## 59) texture_mean< 2.656737 74 1 B (0.01351351 0.98648649)
## 118) smoothness_mean>=-2.074653 3 1 B (0.33333333 0.66666667) *
## 119) smoothness_mean< -2.074653 71 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.468758 100 0 B (0.00000000 1.00000000) *
##
## $trees[[3]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 415 B (0.45504386 0.54495614)
## 2) texture_worst>=4.275472 677 300 M (0.55686854 0.44313146)
## 4) smoothness_worst>=-1.556752 487 165 M (0.66119097 0.33880903)
## 8) symmetry_worst>=-2.027922 424 123 M (0.70990566 0.29009434)
## 16) smoothness_mean>=-2.469882 410 110 M (0.73170732 0.26829268)
## 32) symmetry_worst>=-1.329407 41 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -1.329407 369 110 M (0.70189702 0.29810298)
## 66) smoothness_worst< -1.51308 116 19 M (0.83620690 0.16379310) *
## 67) smoothness_worst>=-1.51308 253 91 M (0.64031621 0.35968379) *
## 17) smoothness_mean< -2.469882 14 1 B (0.07142857 0.92857143)
## 34) compactness_se>=-3.935569 1 0 M (1.00000000 0.00000000) *
## 35) compactness_se< -3.935569 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.027922 63 21 B (0.33333333 0.66666667)
## 18) texture_worst>=4.583884 42 21 M (0.50000000 0.50000000)
## 36) texture_worst< 5.117452 29 8 M (0.72413793 0.27586207)
## 72) compactness_se>=-4.170636 24 4 M (0.83333333 0.16666667) *
## 73) compactness_se< -4.170636 5 1 B (0.20000000 0.80000000) *
## 37) texture_worst>=5.117452 13 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 4.583884 21 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.556752 190 55 B (0.28947368 0.71052632)
## 10) symmetry_worst< -2.227786 23 7 M (0.69565217 0.30434783)
## 20) smoothness_mean>=-2.484324 18 2 M (0.88888889 0.11111111)
## 40) texture_mean< 3.379986 17 1 M (0.94117647 0.05882353)
## 80) smoothness_mean>=-2.401038 12 0 M (1.00000000 0.00000000) *
## 81) smoothness_mean< -2.401038 5 1 M (0.80000000 0.20000000) *
## 41) texture_mean>=3.379986 1 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.484324 5 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-2.227786 167 39 B (0.23353293 0.76646707)
## 22) symmetry_worst>=-1.238986 6 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst< -1.238986 161 33 B (0.20496894 0.79503106)
## 46) texture_worst>=5.066446 15 6 M (0.60000000 0.40000000)
## 92) texture_mean< 3.332536 6 0 M (1.00000000 0.00000000) *
## 93) texture_mean>=3.332536 9 3 B (0.33333333 0.66666667) *
## 47) texture_worst< 5.066446 146 24 B (0.16438356 0.83561644)
## 94) smoothness_worst< -1.720903 7 2 M (0.71428571 0.28571429) *
## 95) smoothness_worst>=-1.720903 139 19 B (0.13669065 0.86330935) *
## 3) texture_worst< 4.275472 235 38 B (0.16170213 0.83829787)
## 6) compactness_se>=-3.958868 105 38 B (0.36190476 0.63809524)
## 12) symmetry_worst>=-1.42974 28 10 M (0.64285714 0.35714286)
## 24) compactness_se>=-3.391558 13 0 M (1.00000000 0.00000000) *
## 25) compactness_se< -3.391558 15 5 B (0.33333333 0.66666667)
## 50) smoothness_mean< -2.393992 5 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.393992 10 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.42974 77 20 B (0.25974026 0.74025974)
## 26) compactness_se< -3.492332 43 19 B (0.44186047 0.55813953)
## 52) compactness_se>=-3.764682 26 9 M (0.65384615 0.34615385)
## 104) texture_mean< 2.716348 14 0 M (1.00000000 0.00000000) *
## 105) texture_mean>=2.716348 12 3 B (0.25000000 0.75000000) *
## 53) compactness_se< -3.764682 17 2 B (0.11764706 0.88235294)
## 106) texture_worst>=4.134459 4 2 M (0.50000000 0.50000000) *
## 107) texture_worst< 4.134459 13 0 B (0.00000000 1.00000000) *
## 27) compactness_se>=-3.492332 34 1 B (0.02941176 0.97058824)
## 54) compactness_se< -3.48221 6 1 B (0.16666667 0.83333333)
## 108) texture_mean< 2.787307 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.787307 5 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-3.48221 28 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.958868 130 0 B (0.00000000 1.00000000) *
##
## $trees[[4]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 364 B (0.39912281 0.60087719)
## 2) compactness_se>=-3.721197 380 152 M (0.60000000 0.40000000)
## 4) texture_mean>=3.054236 164 40 M (0.75609756 0.24390244)
## 8) symmetry_worst>=-2.029591 130 20 M (0.84615385 0.15384615)
## 16) smoothness_mean>=-2.41714 98 6 M (0.93877551 0.06122449)
## 32) smoothness_mean< -2.105484 92 3 M (0.96739130 0.03260870)
## 64) smoothness_worst>=-1.609426 91 2 M (0.97802198 0.02197802) *
## 65) smoothness_worst< -1.609426 1 0 B (0.00000000 1.00000000) *
## 33) smoothness_mean>=-2.105484 6 3 M (0.50000000 0.50000000)
## 66) texture_mean< 3.186512 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.186512 3 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.41714 32 14 M (0.56250000 0.43750000)
## 34) smoothness_mean< -2.453967 19 1 M (0.94736842 0.05263158)
## 68) smoothness_worst>=-1.612487 18 0 M (1.00000000 0.00000000) *
## 69) smoothness_worst< -1.612487 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.453967 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.029591 34 14 B (0.41176471 0.58823529)
## 18) texture_worst< 4.645452 10 0 M (1.00000000 0.00000000) *
## 19) texture_worst>=4.645452 24 4 B (0.16666667 0.83333333)
## 38) symmetry_worst< -2.242858 6 2 M (0.66666667 0.33333333)
## 76) texture_mean>=3.208081 4 0 M (1.00000000 0.00000000) *
## 77) texture_mean< 3.208081 2 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst>=-2.242858 18 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 3.054236 216 104 B (0.48148148 0.51851852)
## 10) compactness_se< -3.427747 107 31 M (0.71028037 0.28971963)
## 20) texture_mean>=2.628033 99 23 M (0.76767677 0.23232323)
## 40) texture_mean< 3.040702 92 16 M (0.82608696 0.17391304)
## 80) smoothness_mean>=-2.443631 81 8 M (0.90123457 0.09876543) *
## 81) smoothness_mean< -2.443631 11 3 B (0.27272727 0.72727273) *
## 41) texture_mean>=3.040702 7 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.628033 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-3.427747 109 28 B (0.25688073 0.74311927)
## 22) symmetry_worst>=-1.300369 16 1 M (0.93750000 0.06250000)
## 44) compactness_se< -2.524297 15 0 M (1.00000000 0.00000000) *
## 45) compactness_se>=-2.524297 1 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.300369 93 13 B (0.13978495 0.86021505)
## 46) symmetry_worst>=-1.853888 48 13 B (0.27083333 0.72916667)
## 92) texture_mean>=2.96681 19 9 M (0.52631579 0.47368421) *
## 93) texture_mean< 2.96681 29 3 B (0.10344828 0.89655172) *
## 47) symmetry_worst< -1.853888 45 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 532 136 B (0.25563910 0.74436090)
## 6) texture_worst>=4.36289 386 128 B (0.33160622 0.66839378)
## 12) smoothness_worst>=-1.424105 30 5 M (0.83333333 0.16666667)
## 24) smoothness_mean>=-2.397334 25 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.397334 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.424105 356 103 B (0.28932584 0.71067416)
## 26) smoothness_mean< -2.260964 281 99 B (0.35231317 0.64768683)
## 52) smoothness_mean>=-2.272056 8 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.272056 273 91 B (0.33333333 0.66666667)
## 106) compactness_se< -4.039628 175 74 B (0.42285714 0.57714286) *
## 107) compactness_se>=-4.039628 98 17 B (0.17346939 0.82653061) *
## 27) smoothness_mean>=-2.260964 75 4 B (0.05333333 0.94666667)
## 54) texture_mean< 2.844609 2 1 M (0.50000000 0.50000000)
## 108) texture_mean>=2.831705 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.831705 1 0 B (0.00000000 1.00000000) *
## 55) texture_mean>=2.844609 73 3 B (0.04109589 0.95890411)
## 110) symmetry_worst>=-1.611386 23 3 B (0.13043478 0.86956522) *
## 111) symmetry_worst< -1.611386 50 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.36289 146 8 B (0.05479452 0.94520548)
## 14) symmetry_worst>=-1.428729 6 2 M (0.66666667 0.33333333)
## 28) texture_mean>=2.772337 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.772337 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.428729 140 4 B (0.02857143 0.97142857)
## 30) smoothness_mean>=-2.081877 2 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.081877 138 2 B (0.01449275 0.98550725)
## 62) compactness_se>=-3.892047 15 2 B (0.13333333 0.86666667)
## 124) compactness_se< -3.878107 2 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.878107 13 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.892047 123 0 B (0.00000000 1.00000000) *
##
## $trees[[5]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 444 B (0.48684211 0.51315789)
## 2) texture_mean>=2.811204 751 326 M (0.56591212 0.43408788)
## 4) compactness_se>=-4.779408 733 308 M (0.57980900 0.42019100)
## 8) texture_mean>=3.116842 176 46 M (0.73863636 0.26136364)
## 16) smoothness_mean>=-2.489159 154 30 M (0.80519481 0.19480519)
## 32) compactness_se>=-4.543049 146 22 M (0.84931507 0.15068493)
## 64) smoothness_mean< -2.099273 141 17 M (0.87943262 0.12056738) *
## 65) smoothness_mean>=-2.099273 5 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.543049 8 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.489159 22 6 B (0.27272727 0.72727273)
## 34) texture_mean< 3.190706 6 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=3.190706 16 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 3.116842 557 262 M (0.52962298 0.47037702)
## 18) symmetry_worst>=-1.107986 24 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst< -1.107986 533 262 M (0.50844278 0.49155722)
## 38) symmetry_worst>=-1.862978 386 170 M (0.55958549 0.44041451)
## 76) texture_mean< 3.11507 374 158 M (0.57754011 0.42245989) *
## 77) texture_mean>=3.11507 12 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst< -1.862978 147 55 B (0.37414966 0.62585034)
## 78) texture_worst>=4.905691 8 0 M (1.00000000 0.00000000) *
## 79) texture_worst< 4.905691 139 47 B (0.33812950 0.66187050) *
## 5) compactness_se< -4.779408 18 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.811204 161 19 B (0.11801242 0.88198758)
## 6) symmetry_worst>=-1.281003 5 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.281003 156 14 B (0.08974359 0.91025641)
## 14) smoothness_mean>=-1.977294 4 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -1.977294 152 10 B (0.06578947 0.93421053)
## 30) smoothness_mean>=-2.321264 59 9 B (0.15254237 0.84745763)
## 60) smoothness_mean< -2.287239 22 9 B (0.40909091 0.59090909)
## 120) texture_worst>=4.138116 5 0 M (1.00000000 0.00000000) *
## 121) texture_worst< 4.138116 17 4 B (0.23529412 0.76470588) *
## 61) smoothness_mean>=-2.287239 37 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.321264 93 1 B (0.01075269 0.98924731)
## 62) compactness_se>=-3.488718 12 1 B (0.08333333 0.91666667)
## 124) compactness_se< -3.483667 1 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.483667 11 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.488718 81 0 B (0.00000000 1.00000000) *
##
## $trees[[6]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 393 B (0.43092105 0.56907895)
## 2) texture_mean>=3.058002 294 107 M (0.63605442 0.36394558)
## 4) symmetry_worst>=-1.71268 124 21 M (0.83064516 0.16935484)
## 8) texture_worst>=4.818867 96 7 M (0.92708333 0.07291667)
## 16) smoothness_mean>=-2.509617 95 6 M (0.93684211 0.06315789)
## 32) smoothness_mean>=-2.334545 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.334545 41 6 M (0.85365854 0.14634146)
## 66) smoothness_mean< -2.347634 38 3 M (0.92105263 0.07894737) *
## 67) smoothness_mean>=-2.347634 3 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.509617 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.818867 28 14 M (0.50000000 0.50000000)
## 18) texture_worst< 4.790105 13 0 M (1.00000000 0.00000000) *
## 19) texture_worst>=4.790105 15 1 B (0.06666667 0.93333333)
## 38) smoothness_mean< -2.321477 1 0 M (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.321477 14 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.71268 170 84 B (0.49411765 0.50588235)
## 10) symmetry_worst< -1.733593 145 61 M (0.57931034 0.42068966)
## 20) smoothness_worst>=-1.603555 118 38 M (0.67796610 0.32203390)
## 40) symmetry_worst>=-2.184494 91 20 M (0.78021978 0.21978022)
## 80) smoothness_worst< -1.415354 81 11 M (0.86419753 0.13580247) *
## 81) smoothness_worst>=-1.415354 10 1 B (0.10000000 0.90000000) *
## 41) symmetry_worst< -2.184494 27 9 B (0.33333333 0.66666667)
## 82) smoothness_mean< -2.437515 6 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.437515 21 3 B (0.14285714 0.85714286) *
## 21) smoothness_worst< -1.603555 27 4 B (0.14814815 0.85185185)
## 42) smoothness_mean>=-2.373736 3 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.373736 24 1 B (0.04166667 0.95833333)
## 86) texture_worst< 4.508695 2 1 M (0.50000000 0.50000000) *
## 87) texture_worst>=4.508695 22 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.733593 25 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.058002 618 206 B (0.33333333 0.66666667)
## 6) texture_mean>=2.709047 536 200 B (0.37313433 0.62686567)
## 12) smoothness_worst>=-1.374428 14 1 M (0.92857143 0.07142857)
## 24) symmetry_worst>=-1.846189 13 0 M (1.00000000 0.00000000) *
## 25) symmetry_worst< -1.846189 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.374428 522 187 B (0.35823755 0.64176245)
## 26) compactness_se>=-4.717333 489 186 B (0.38036810 0.61963190)
## 52) compactness_se< -4.594248 32 9 M (0.71875000 0.28125000)
## 104) smoothness_worst< -1.54201 27 4 M (0.85185185 0.14814815) *
## 105) smoothness_worst>=-1.54201 5 0 B (0.00000000 1.00000000) *
## 53) compactness_se>=-4.594248 457 163 B (0.35667396 0.64332604)
## 106) smoothness_mean>=-2.434347 375 150 B (0.40000000 0.60000000) *
## 107) smoothness_mean< -2.434347 82 13 B (0.15853659 0.84146341) *
## 27) compactness_se< -4.717333 33 1 B (0.03030303 0.96969697)
## 54) texture_mean>=2.991714 1 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 2.991714 32 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.709047 82 6 B (0.07317073 0.92682927)
## 14) symmetry_worst>=-1.122487 2 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst< -1.122487 80 4 B (0.05000000 0.95000000)
## 30) texture_mean< 2.487336 8 2 B (0.25000000 0.75000000)
## 60) texture_mean>=2.434062 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.434062 6 0 B (0.00000000 1.00000000) *
## 31) texture_mean>=2.487336 72 2 B (0.02777778 0.97222222)
## 62) symmetry_worst< -2.111279 14 2 B (0.14285714 0.85714286)
## 124) smoothness_worst>=-1.49704 2 0 M (1.00000000 0.00000000) *
## 125) smoothness_worst< -1.49704 12 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-2.111279 58 0 B (0.00000000 1.00000000) *
##
## $trees[[7]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 M (0.51206140 0.48793860)
## 2) texture_mean>=2.707375 842 379 M (0.54988124 0.45011876)
## 4) compactness_se>=-4.706178 803 340 M (0.57658780 0.42341220)
## 8) symmetry_worst>=-1.329407 50 7 M (0.86000000 0.14000000)
## 16) texture_mean< 3.099059 39 1 M (0.97435897 0.02564103)
## 32) texture_mean>=2.756192 38 0 M (1.00000000 0.00000000) *
## 33) texture_mean< 2.756192 1 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.099059 11 5 B (0.45454545 0.54545455)
## 34) texture_mean>=3.141437 5 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.141437 6 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.329407 753 333 M (0.55776892 0.44223108)
## 18) smoothness_mean>=-2.546123 734 315 M (0.57084469 0.42915531)
## 36) symmetry_worst< -1.925345 193 63 M (0.67357513 0.32642487)
## 72) smoothness_worst>=-1.604472 169 44 M (0.73964497 0.26035503) *
## 73) smoothness_worst< -1.604472 24 5 B (0.20833333 0.79166667) *
## 37) symmetry_worst>=-1.925345 541 252 M (0.53419593 0.46580407)
## 74) smoothness_worst< -1.596198 49 8 M (0.83673469 0.16326531) *
## 75) smoothness_worst>=-1.596198 492 244 M (0.50406504 0.49593496) *
## 19) smoothness_mean< -2.546123 19 1 B (0.05263158 0.94736842)
## 38) smoothness_worst< -1.720903 4 1 B (0.25000000 0.75000000)
## 76) compactness_se>=-3.013033 1 0 M (1.00000000 0.00000000) *
## 77) compactness_se< -3.013033 3 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.720903 15 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -4.706178 39 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.707375 70 4 B (0.05714286 0.94285714)
## 6) symmetry_worst>=-1.15097 2 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.15097 68 2 B (0.02941176 0.97058824)
## 14) smoothness_mean>=-2.074653 3 1 M (0.66666667 0.33333333)
## 28) texture_mean>=2.434062 2 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.434062 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.074653 65 0 B (0.00000000 1.00000000) *
##
## $trees[[8]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) texture_mean>=2.709047 846 423 M (0.50000000 0.50000000)
## 4) symmetry_worst>=-1.329407 52 6 M (0.88461538 0.11538462)
## 8) texture_mean>=2.742062 50 4 M (0.92000000 0.08000000)
## 16) texture_mean< 3.10949 38 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.10949 12 4 M (0.66666667 0.33333333)
## 34) texture_mean>=3.126045 8 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.126045 4 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.742062 2 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.329407 794 377 B (0.47481108 0.52518892)
## 10) compactness_se>=-4.705732 765 377 B (0.49281046 0.50718954)
## 20) compactness_se< -4.448167 113 30 M (0.73451327 0.26548673)
## 40) smoothness_mean< -2.295268 101 18 M (0.82178218 0.17821782)
## 80) symmetry_worst< -1.478833 95 12 M (0.87368421 0.12631579) *
## 81) symmetry_worst>=-1.478833 6 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.295268 12 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.448167 652 294 B (0.45092025 0.54907975)
## 42) texture_worst< 4.642157 366 177 M (0.51639344 0.48360656)
## 84) smoothness_worst>=-1.456304 54 7 M (0.87037037 0.12962963) *
## 85) smoothness_worst< -1.456304 312 142 B (0.45512821 0.54487179) *
## 43) texture_worst>=4.642157 286 105 B (0.36713287 0.63286713)
## 86) texture_worst>=5.03133 54 21 M (0.61111111 0.38888889) *
## 87) texture_worst< 5.03133 232 72 B (0.31034483 0.68965517) *
## 11) compactness_se< -4.705732 29 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.709047 66 6 B (0.09090909 0.90909091)
## 6) symmetry_worst>=-1.122487 3 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.122487 63 3 B (0.04761905 0.95238095)
## 14) smoothness_mean>=-2.074653 10 2 B (0.20000000 0.80000000)
## 28) smoothness_mean< -2.060513 2 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.060513 8 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.074653 53 1 B (0.01886792 0.98113208)
## 30) symmetry_worst< -2.105665 6 1 B (0.16666667 0.83333333)
## 60) smoothness_mean>=-2.312057 1 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.312057 5 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.105665 47 0 B (0.00000000 1.00000000) *
##
## $trees[[9]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 B (0.48464912 0.51535088)
## 2) texture_mean>=2.824054 787 373 M (0.52604828 0.47395172)
## 4) texture_worst< 4.982753 644 274 M (0.57453416 0.42546584)
## 8) compactness_se>=-4.705732 622 254 M (0.59163987 0.40836013)
## 16) smoothness_mean< -2.352368 300 93 M (0.69000000 0.31000000)
## 32) compactness_se< -4.099264 144 24 M (0.83333333 0.16666667)
## 64) texture_mean>=2.947329 93 6 M (0.93548387 0.06451613) *
## 65) texture_mean< 2.947329 51 18 M (0.64705882 0.35294118) *
## 33) compactness_se>=-4.099264 156 69 M (0.55769231 0.44230769)
## 66) smoothness_mean>=-2.394871 63 10 M (0.84126984 0.15873016) *
## 67) smoothness_mean< -2.394871 93 34 B (0.36559140 0.63440860) *
## 17) smoothness_mean>=-2.352368 322 161 M (0.50000000 0.50000000)
## 34) symmetry_worst>=-1.529476 58 12 M (0.79310345 0.20689655)
## 68) compactness_se>=-4.127915 46 4 M (0.91304348 0.08695652) *
## 69) compactness_se< -4.127915 12 4 B (0.33333333 0.66666667) *
## 35) symmetry_worst< -1.529476 264 115 B (0.43560606 0.56439394)
## 70) smoothness_mean>=-2.332015 245 115 B (0.46938776 0.53061224) *
## 71) smoothness_mean< -2.332015 19 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.705732 22 2 B (0.09090909 0.90909091)
## 18) symmetry_worst>=-1.179946 2 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst< -1.179946 20 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.982753 143 44 B (0.30769231 0.69230769)
## 10) compactness_se>=-3.334337 23 6 M (0.73913043 0.26086957)
## 20) texture_worst>=4.998431 17 0 M (1.00000000 0.00000000) *
## 21) texture_worst< 4.998431 6 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.334337 120 27 B (0.22500000 0.77500000)
## 22) smoothness_mean>=-2.362094 47 19 B (0.40425532 0.59574468)
## 44) smoothness_worst< -1.450409 22 5 M (0.77272727 0.22727273)
## 88) symmetry_worst>=-2.207988 17 0 M (1.00000000 0.00000000) *
## 89) symmetry_worst< -2.207988 5 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.450409 25 2 B (0.08000000 0.92000000)
## 90) symmetry_worst>=-1.24413 1 0 M (1.00000000 0.00000000) *
## 91) symmetry_worst< -1.24413 24 1 B (0.04166667 0.95833333) *
## 23) smoothness_mean< -2.362094 73 8 B (0.10958904 0.89041096)
## 46) symmetry_worst>=-1.554775 3 0 M (1.00000000 0.00000000) *
## 47) symmetry_worst< -1.554775 70 5 B (0.07142857 0.92857143)
## 94) texture_worst>=5.636459 1 0 M (1.00000000 0.00000000) *
## 95) texture_worst< 5.636459 69 4 B (0.05797101 0.94202899) *
## 3) texture_mean< 2.824054 125 28 B (0.22400000 0.77600000)
## 6) compactness_se>=-3.764682 55 26 B (0.47272727 0.52727273)
## 12) smoothness_mean>=-2.31958 36 13 M (0.63888889 0.36111111)
## 24) texture_worst>=3.973898 24 4 M (0.83333333 0.16666667)
## 48) compactness_se< -3.364454 14 0 M (1.00000000 0.00000000) *
## 49) compactness_se>=-3.364454 10 4 M (0.60000000 0.40000000)
## 98) symmetry_worst>=-1.316602 6 0 M (1.00000000 0.00000000) *
## 99) symmetry_worst< -1.316602 4 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.973898 12 3 B (0.25000000 0.75000000)
## 50) compactness_se< -3.688804 2 0 M (1.00000000 0.00000000) *
## 51) compactness_se>=-3.688804 10 1 B (0.10000000 0.90000000)
## 102) texture_mean< 2.366153 1 0 M (1.00000000 0.00000000) *
## 103) texture_mean>=2.366153 9 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.31958 19 3 B (0.15789474 0.84210526)
## 26) symmetry_worst< -1.982852 3 1 M (0.66666667 0.33333333)
## 52) texture_mean>=2.763153 2 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.763153 1 0 B (0.00000000 1.00000000) *
## 27) symmetry_worst>=-1.982852 16 1 B (0.06250000 0.93750000)
## 54) smoothness_worst>=-1.493125 2 1 M (0.50000000 0.50000000)
## 108) texture_mean< 2.774841 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.774841 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst< -1.493125 14 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.764682 70 2 B (0.02857143 0.97142857)
## 14) symmetry_worst>=-1.431268 3 1 B (0.33333333 0.66666667)
## 28) texture_mean>=2.799919 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.799919 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.431268 67 1 B (0.01492537 0.98507463)
## 30) compactness_se>=-3.894783 8 1 B (0.12500000 0.87500000)
## 60) compactness_se< -3.866661 1 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-3.866661 7 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.894783 59 0 B (0.00000000 1.00000000) *
##
## $trees[[10]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 M (0.51973684 0.48026316)
## 2) texture_worst>=4.260219 794 348 M (0.56171285 0.43828715)
## 4) compactness_se< -2.927016 740 305 M (0.58783784 0.41216216)
## 8) compactness_se>=-3.322182 70 9 M (0.87142857 0.12857143)
## 16) smoothness_worst>=-1.507356 35 0 M (1.00000000 0.00000000) *
## 17) smoothness_worst< -1.507356 35 9 M (0.74285714 0.25714286)
## 34) smoothness_worst< -1.51411 28 2 M (0.92857143 0.07142857)
## 68) texture_mean>=2.988153 26 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.988153 2 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.51411 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.322182 670 296 M (0.55820896 0.44179104)
## 18) texture_worst< 4.642157 389 145 M (0.62724936 0.37275064)
## 36) symmetry_worst< -1.559263 324 102 M (0.68518519 0.31481481)
## 72) compactness_se>=-4.614925 306 84 M (0.72549020 0.27450980) *
## 73) compactness_se< -4.614925 18 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst>=-1.559263 65 22 B (0.33846154 0.66153846)
## 74) texture_worst>=4.614159 15 1 M (0.93333333 0.06666667) *
## 75) texture_worst< 4.614159 50 8 B (0.16000000 0.84000000) *
## 19) texture_worst>=4.642157 281 130 B (0.46263345 0.53736655)
## 38) symmetry_worst>=-1.591238 69 17 M (0.75362319 0.24637681)
## 76) texture_mean< 3.095125 45 3 M (0.93333333 0.06666667) *
## 77) texture_mean>=3.095125 24 10 B (0.41666667 0.58333333) *
## 39) symmetry_worst< -1.591238 212 78 B (0.36792453 0.63207547)
## 78) texture_worst>=4.682677 181 78 B (0.43093923 0.56906077) *
## 79) texture_worst< 4.682677 31 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-2.927016 54 11 B (0.20370370 0.79629630)
## 10) smoothness_worst>=-1.397207 7 0 M (1.00000000 0.00000000) *
## 11) smoothness_worst< -1.397207 47 4 B (0.08510638 0.91489362)
## 22) symmetry_worst< -2.040594 3 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst>=-2.040594 44 1 B (0.02272727 0.97727273)
## 46) texture_mean>=3.063534 5 1 B (0.20000000 0.80000000)
## 92) texture_mean< 3.166628 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean>=3.166628 4 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 3.063534 39 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.260219 118 28 B (0.23728814 0.76271186)
## 6) compactness_se>=-3.97985 73 28 B (0.38356164 0.61643836)
## 12) texture_mean>=2.863053 9 1 M (0.88888889 0.11111111)
## 24) smoothness_mean>=-2.316299 8 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.316299 1 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.863053 64 20 B (0.31250000 0.68750000)
## 26) symmetry_worst>=-1.281003 5 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst< -1.281003 59 15 B (0.25423729 0.74576271)
## 54) compactness_se< -3.866661 7 1 M (0.85714286 0.14285714)
## 108) texture_worst>=4.110502 6 0 M (1.00000000 0.00000000) *
## 109) texture_worst< 4.110502 1 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-3.866661 52 9 B (0.17307692 0.82692308)
## 110) texture_mean< 2.525679 3 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.525679 49 6 B (0.12244898 0.87755102) *
## 7) compactness_se< -3.97985 45 0 B (0.00000000 1.00000000) *
##
## $trees[[11]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 419 M (0.54057018 0.45942982)
## 2) texture_worst>=4.517889 608 231 M (0.62006579 0.37993421)
## 4) texture_worst< 4.54138 80 6 M (0.92500000 0.07500000)
## 8) symmetry_worst< -1.433387 78 4 M (0.94871795 0.05128205)
## 16) compactness_se< -3.16075 75 2 M (0.97333333 0.02666667)
## 32) smoothness_mean>=-2.440377 74 1 M (0.98648649 0.01351351)
## 64) texture_mean< 3.086942 68 0 M (1.00000000 0.00000000) *
## 65) texture_mean>=3.086942 6 1 M (0.83333333 0.16666667) *
## 33) smoothness_mean< -2.440377 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.16075 3 1 B (0.33333333 0.66666667)
## 34) texture_mean>=3.023554 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.023554 2 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.433387 2 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.54138 528 225 M (0.57386364 0.42613636)
## 10) texture_worst>=4.569492 494 192 M (0.61133603 0.38866397)
## 20) compactness_se>=-4.505325 423 144 M (0.65957447 0.34042553)
## 40) smoothness_worst< -1.400053 388 119 M (0.69329897 0.30670103)
## 80) smoothness_worst>=-1.672049 381 112 M (0.70603675 0.29396325) *
## 81) smoothness_worst< -1.672049 7 0 B (0.00000000 1.00000000) *
## 41) smoothness_worst>=-1.400053 35 10 B (0.28571429 0.71428571)
## 82) texture_mean>=3.044522 9 1 M (0.88888889 0.11111111) *
## 83) texture_mean< 3.044522 26 2 B (0.07692308 0.92307692) *
## 21) compactness_se< -4.505325 71 23 B (0.32394366 0.67605634)
## 42) smoothness_worst< -1.549205 36 16 M (0.55555556 0.44444444)
## 84) symmetry_worst>=-1.909332 26 6 M (0.76923077 0.23076923) *
## 85) symmetry_worst< -1.909332 10 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst>=-1.549205 35 3 B (0.08571429 0.91428571)
## 86) symmetry_worst< -1.696111 12 3 B (0.25000000 0.75000000) *
## 87) symmetry_worst>=-1.696111 23 0 B (0.00000000 1.00000000) *
## 11) texture_worst< 4.569492 34 1 B (0.02941176 0.97058824)
## 22) texture_mean>=3.034949 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.034949 33 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.517889 304 116 B (0.38157895 0.61842105)
## 6) compactness_se>=-3.766631 127 59 M (0.53543307 0.46456693)
## 12) smoothness_mean>=-2.454939 106 40 M (0.62264151 0.37735849)
## 24) compactness_se< -3.427747 57 12 M (0.78947368 0.21052632)
## 48) symmetry_worst< -1.461208 52 7 M (0.86538462 0.13461538)
## 96) texture_worst< 4.460444 50 5 M (0.90000000 0.10000000) *
## 97) texture_worst>=4.460444 2 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst>=-1.461208 5 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.427747 49 21 B (0.42857143 0.57142857)
## 50) smoothness_mean>=-2.149436 10 1 M (0.90000000 0.10000000)
## 100) compactness_se>=-3.412571 9 0 M (1.00000000 0.00000000) *
## 101) compactness_se< -3.412571 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.149436 39 12 B (0.30769231 0.69230769)
## 102) texture_mean>=2.96681 9 1 M (0.88888889 0.11111111) *
## 103) texture_mean< 2.96681 30 4 B (0.13333333 0.86666667) *
## 13) smoothness_mean< -2.454939 21 2 B (0.09523810 0.90476190)
## 26) texture_mean>=3.038737 2 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 3.038737 19 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.766631 177 48 B (0.27118644 0.72881356)
## 14) texture_mean>=2.812409 114 47 B (0.41228070 0.58771930)
## 28) smoothness_worst>=-1.451541 14 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.451541 100 33 B (0.33000000 0.67000000)
## 58) smoothness_worst< -1.538735 64 31 M (0.51562500 0.48437500)
## 116) symmetry_worst< -1.548429 54 21 M (0.61111111 0.38888889) *
## 117) symmetry_worst>=-1.548429 10 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst>=-1.538735 36 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 2.812409 63 1 B (0.01587302 0.98412698)
## 30) symmetry_worst< -1.930267 13 1 B (0.07692308 0.92307692)
## 60) symmetry_worst>=-1.95343 1 0 M (1.00000000 0.00000000) *
## 61) symmetry_worst< -1.95343 12 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.930267 50 0 B (0.00000000 1.00000000) *
##
## $trees[[12]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) smoothness_mean>=-2.424301 659 326 M (0.50531108 0.49468892)
## 4) smoothness_mean< -2.382983 117 39 M (0.66666667 0.33333333)
## 8) texture_worst>=4.611234 68 5 M (0.92647059 0.07352941)
## 16) smoothness_worst>=-1.586424 64 2 M (0.96875000 0.03125000)
## 32) symmetry_worst>=-2.212871 62 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.586424 4 1 B (0.25000000 0.75000000)
## 34) smoothness_mean>=-2.4008 1 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.4008 3 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.611234 49 15 B (0.30612245 0.69387755)
## 18) smoothness_mean< -2.411844 16 4 M (0.75000000 0.25000000)
## 36) smoothness_worst< -1.538735 10 0 M (1.00000000 0.00000000) *
## 37) smoothness_worst>=-1.538735 6 2 B (0.33333333 0.66666667)
## 74) smoothness_mean< -2.421763 2 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean>=-2.421763 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.411844 33 3 B (0.09090909 0.90909091)
## 38) texture_mean>=2.964668 4 1 M (0.75000000 0.25000000)
## 76) smoothness_mean>=-2.394659 3 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.394659 1 0 B (0.00000000 1.00000000) *
## 39) texture_mean< 2.964668 29 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.382983 542 255 B (0.47047970 0.52952030)
## 10) compactness_se>=-4.025757 373 169 M (0.54691689 0.45308311)
## 20) smoothness_mean>=-2.333148 292 111 M (0.61986301 0.38013699)
## 40) symmetry_worst>=-1.839419 204 56 M (0.72549020 0.27450980)
## 80) texture_worst>=4.508732 140 21 M (0.85000000 0.15000000) *
## 81) texture_worst< 4.508732 64 29 B (0.45312500 0.54687500) *
## 41) symmetry_worst< -1.839419 88 33 B (0.37500000 0.62500000)
## 82) symmetry_worst< -1.925345 62 30 M (0.51612903 0.48387097) *
## 83) symmetry_worst>=-1.925345 26 1 B (0.03846154 0.96153846) *
## 21) smoothness_mean< -2.333148 81 23 B (0.28395062 0.71604938)
## 42) symmetry_worst< -1.571577 52 23 B (0.44230769 0.55769231)
## 84) symmetry_worst>=-1.716495 18 3 M (0.83333333 0.16666667) *
## 85) symmetry_worst< -1.716495 34 8 B (0.23529412 0.76470588) *
## 43) symmetry_worst>=-1.571577 29 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.025757 169 51 B (0.30177515 0.69822485)
## 22) smoothness_mean< -2.294121 82 36 M (0.56097561 0.43902439)
## 44) texture_worst>=4.376622 69 23 M (0.66666667 0.33333333)
## 88) texture_worst< 4.626933 29 0 M (1.00000000 0.00000000) *
## 89) texture_worst>=4.626933 40 17 B (0.42500000 0.57500000) *
## 45) texture_worst< 4.376622 13 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.294121 87 5 B (0.05747126 0.94252874)
## 46) smoothness_mean>=-2.21595 20 5 B (0.25000000 0.75000000)
## 92) smoothness_mean< -2.210016 6 1 M (0.83333333 0.16666667) *
## 93) smoothness_mean>=-2.210016 14 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean< -2.21595 67 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.424301 253 77 B (0.30434783 0.69565217)
## 6) texture_mean>=2.963209 154 65 B (0.42207792 0.57792208)
## 12) texture_mean< 3.176386 107 48 M (0.55140187 0.44859813)
## 24) texture_mean>=3.129791 26 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.129791 81 33 B (0.40740741 0.59259259)
## 50) symmetry_worst>=-1.54778 10 0 M (1.00000000 0.00000000) *
## 51) symmetry_worst< -1.54778 71 23 B (0.32394366 0.67605634)
## 102) smoothness_mean< -2.478376 44 21 M (0.52272727 0.47727273) *
## 103) smoothness_mean>=-2.478376 27 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=3.176386 47 6 B (0.12765957 0.87234043)
## 26) symmetry_worst>=-1.530091 3 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst< -1.530091 44 3 B (0.06818182 0.93181818)
## 54) symmetry_worst< -2.188379 3 1 M (0.66666667 0.33333333)
## 108) texture_mean>=3.330945 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 3.330945 1 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst>=-2.188379 41 1 B (0.02439024 0.97560976)
## 110) smoothness_worst>=-1.490267 3 1 B (0.33333333 0.66666667) *
## 111) smoothness_worst< -1.490267 38 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.963209 99 12 B (0.12121212 0.87878788)
## 14) smoothness_worst>=-1.554151 29 8 B (0.27586207 0.72413793)
## 28) smoothness_worst< -1.551775 8 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.551775 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.554151 70 4 B (0.05714286 0.94285714)
## 30) compactness_se>=-3.615179 16 4 B (0.25000000 0.75000000)
## 60) texture_mean>=2.935975 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.935975 13 1 B (0.07692308 0.92307692)
## 122) symmetry_worst< -1.998079 1 0 M (1.00000000 0.00000000) *
## 123) symmetry_worst>=-1.998079 12 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.615179 54 0 B (0.00000000 1.00000000) *
##
## $trees[[13]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) smoothness_mean>=-2.423454 652 318 M (0.51226994 0.48773006)
## 4) smoothness_mean< -2.312434 297 117 M (0.60606061 0.39393939)
## 8) symmetry_worst< -1.608146 236 69 M (0.70762712 0.29237288)
## 16) smoothness_worst>=-1.559798 179 38 M (0.78770950 0.21229050)
## 32) smoothness_mean>=-2.416986 167 26 M (0.84431138 0.15568862)
## 64) symmetry_worst>=-2.208456 163 22 M (0.86503067 0.13496933) *
## 65) symmetry_worst< -2.208456 4 0 B (0.00000000 1.00000000) *
## 33) smoothness_mean< -2.416986 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.559798 57 26 B (0.45614035 0.54385965)
## 34) texture_worst>=4.395741 38 12 M (0.68421053 0.31578947)
## 68) smoothness_worst>=-1.586874 29 5 M (0.82758621 0.17241379) *
## 69) smoothness_worst< -1.586874 9 2 B (0.22222222 0.77777778) *
## 35) texture_worst< 4.395741 19 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.608146 61 13 B (0.21311475 0.78688525)
## 18) texture_mean>=3.067477 15 5 M (0.66666667 0.33333333)
## 36) symmetry_worst>=-1.551105 11 1 M (0.90909091 0.09090909)
## 72) compactness_se>=-4.507761 10 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.507761 1 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.551105 4 0 B (0.00000000 1.00000000) *
## 19) texture_mean< 3.067477 46 3 B (0.06521739 0.93478261)
## 38) symmetry_worst>=-1.431522 7 3 B (0.42857143 0.57142857)
## 76) smoothness_mean>=-2.344658 2 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.344658 5 1 B (0.20000000 0.80000000) *
## 39) symmetry_worst< -1.431522 39 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.312434 355 154 B (0.43380282 0.56619718)
## 10) symmetry_worst>=-1.612559 127 46 M (0.63779528 0.36220472)
## 20) smoothness_worst>=-1.500061 96 18 M (0.81250000 0.18750000)
## 40) compactness_se>=-4.214968 92 14 M (0.84782609 0.15217391)
## 80) texture_mean>=2.822248 70 5 M (0.92857143 0.07142857) *
## 81) texture_mean< 2.822248 22 9 M (0.59090909 0.40909091) *
## 41) compactness_se< -4.214968 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.500061 31 3 B (0.09677419 0.90322581)
## 42) texture_mean>=3.137421 1 0 M (1.00000000 0.00000000) *
## 43) texture_mean< 3.137421 30 2 B (0.06666667 0.93333333)
## 86) smoothness_worst< -1.50756 7 2 B (0.28571429 0.71428571) *
## 87) smoothness_worst>=-1.50756 23 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.612559 228 73 B (0.32017544 0.67982456)
## 22) smoothness_worst< -1.567043 13 0 M (1.00000000 0.00000000) *
## 23) smoothness_worst>=-1.567043 215 60 B (0.27906977 0.72093023)
## 46) compactness_se>=-3.4389 52 20 M (0.61538462 0.38461538)
## 92) smoothness_mean< -2.25237 25 2 M (0.92000000 0.08000000) *
## 93) smoothness_mean>=-2.25237 27 9 B (0.33333333 0.66666667) *
## 47) compactness_se< -3.4389 163 28 B (0.17177914 0.82822086)
## 94) compactness_se>=-4.030876 113 27 B (0.23893805 0.76106195) *
## 95) compactness_se< -4.030876 50 1 B (0.02000000 0.98000000) *
## 3) smoothness_mean< -2.423454 260 76 B (0.29230769 0.70769231)
## 6) texture_mean>=2.921008 199 74 B (0.37185930 0.62814070)
## 12) texture_mean< 3.176386 142 68 B (0.47887324 0.52112676)
## 24) texture_mean>=3.130673 27 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.130673 115 41 B (0.35652174 0.64347826)
## 50) smoothness_worst< -1.60795 61 28 M (0.54098361 0.45901639)
## 100) symmetry_worst>=-1.874628 41 9 M (0.78048780 0.21951220) *
## 101) symmetry_worst< -1.874628 20 1 B (0.05000000 0.95000000) *
## 51) smoothness_worst>=-1.60795 54 8 B (0.14814815 0.85185185)
## 102) texture_mean< 2.930359 6 0 M (1.00000000 0.00000000) *
## 103) texture_mean>=2.930359 48 2 B (0.04166667 0.95833333) *
## 13) texture_mean>=3.176386 57 6 B (0.10526316 0.89473684)
## 26) texture_mean>=3.388429 8 3 M (0.62500000 0.37500000)
## 52) smoothness_mean>=-2.520061 5 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.520061 3 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 3.388429 49 1 B (0.02040816 0.97959184)
## 54) smoothness_mean>=-2.425205 1 0 M (1.00000000 0.00000000) *
## 55) smoothness_mean< -2.425205 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.921008 61 2 B (0.03278689 0.96721311)
## 14) texture_worst< 3.92417 4 1 B (0.25000000 0.75000000)
## 28) texture_mean>=2.707858 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.707858 3 0 B (0.00000000 1.00000000) *
## 15) texture_worst>=3.92417 57 1 B (0.01754386 0.98245614)
## 30) texture_worst< 4.400796 22 1 B (0.04545455 0.95454545)
## 60) texture_worst>=4.389974 1 0 M (1.00000000 0.00000000) *
## 61) texture_worst< 4.389974 21 0 B (0.00000000 1.00000000) *
## 31) texture_worst>=4.400796 35 0 B (0.00000000 1.00000000) *
##
## $trees[[14]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) texture_mean>=3.005187 425 172 M (0.59529412 0.40470588)
## 4) texture_mean< 3.02965 48 3 M (0.93750000 0.06250000)
## 8) compactness_se>=-4.262999 46 1 M (0.97826087 0.02173913)
## 16) smoothness_mean>=-2.60159 45 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.60159 1 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.262999 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.02965 377 169 M (0.55172414 0.44827586)
## 10) smoothness_mean>=-2.258569 81 18 M (0.77777778 0.22222222)
## 20) smoothness_mean< -2.099273 68 5 M (0.92647059 0.07352941)
## 40) compactness_se>=-4.045035 63 0 M (1.00000000 0.00000000) *
## 41) compactness_se< -4.045035 5 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.099273 13 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.258569 296 145 B (0.48986486 0.51013514)
## 22) compactness_se>=-3.477231 92 25 M (0.72826087 0.27173913)
## 44) smoothness_worst< -1.468038 73 7 M (0.90410959 0.09589041)
## 88) texture_mean>=3.038537 71 5 M (0.92957746 0.07042254) *
## 89) texture_mean< 3.038537 2 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.468038 19 1 B (0.05263158 0.94736842)
## 90) smoothness_mean>=-2.2833 1 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.2833 18 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.477231 204 78 B (0.38235294 0.61764706)
## 46) compactness_se< -3.872601 117 54 M (0.53846154 0.46153846)
## 92) smoothness_mean< -2.291157 96 36 M (0.62500000 0.37500000) *
## 93) smoothness_mean>=-2.291157 21 3 B (0.14285714 0.85714286) *
## 47) compactness_se>=-3.872601 87 15 B (0.17241379 0.82758621)
## 94) smoothness_worst>=-1.442386 6 0 M (1.00000000 0.00000000) *
## 95) smoothness_worst< -1.442386 81 9 B (0.11111111 0.88888889) *
## 3) texture_mean< 3.005187 487 183 B (0.37577002 0.62422998)
## 6) smoothness_worst>=-1.451541 87 32 M (0.63218391 0.36781609)
## 12) compactness_se>=-4.04059 65 15 M (0.76923077 0.23076923)
## 24) smoothness_worst< -1.349735 54 6 M (0.88888889 0.11111111)
## 48) texture_mean>=2.780541 49 3 M (0.93877551 0.06122449)
## 96) texture_worst< 4.783684 41 0 M (1.00000000 0.00000000) *
## 97) texture_worst>=4.783684 8 3 M (0.62500000 0.37500000) *
## 49) texture_mean< 2.780541 5 2 B (0.40000000 0.60000000)
## 98) smoothness_mean>=-2.150667 2 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean< -2.150667 3 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.349735 11 2 B (0.18181818 0.81818182)
## 50) symmetry_worst>=-1.232339 2 0 M (1.00000000 0.00000000) *
## 51) symmetry_worst< -1.232339 9 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.04059 22 5 B (0.22727273 0.77272727)
## 26) symmetry_worst< -1.750302 5 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst>=-1.750302 17 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.451541 400 128 B (0.32000000 0.68000000)
## 14) texture_worst>=4.83005 10 1 M (0.90000000 0.10000000)
## 28) texture_mean>=2.915217 9 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.915217 1 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.83005 390 119 B (0.30512821 0.69487179)
## 30) smoothness_worst< -1.478565 314 110 B (0.35031847 0.64968153)
## 60) smoothness_worst>=-1.482701 24 3 M (0.87500000 0.12500000)
## 120) smoothness_mean< -2.253991 17 0 M (1.00000000 0.00000000) *
## 121) smoothness_mean>=-2.253991 7 3 M (0.57142857 0.42857143) *
## 61) smoothness_worst< -1.482701 290 89 B (0.30689655 0.69310345)
## 122) symmetry_worst< -1.692331 183 74 B (0.40437158 0.59562842) *
## 123) symmetry_worst>=-1.692331 107 15 B (0.14018692 0.85981308) *
## 31) smoothness_worst>=-1.478565 76 9 B (0.11842105 0.88157895)
## 62) texture_mean>=2.99172 4 0 M (1.00000000 0.00000000) *
## 63) texture_mean< 2.99172 72 5 B (0.06944444 0.93055556)
## 126) compactness_se>=-3.453499 14 4 B (0.28571429 0.71428571) *
## 127) compactness_se< -3.453499 58 1 B (0.01724138 0.98275862) *
##
## $trees[[15]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) smoothness_mean>=-2.489159 806 400 M (0.50372208 0.49627792)
## 4) symmetry_worst< -2.384404 25 3 M (0.88000000 0.12000000)
## 8) texture_mean>=2.861235 22 0 M (1.00000000 0.00000000) *
## 9) texture_mean< 2.861235 3 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-2.384404 781 384 B (0.49167734 0.50832266)
## 10) symmetry_worst>=-2.232873 745 363 M (0.51275168 0.48724832)
## 20) smoothness_mean< -2.473552 21 2 M (0.90476190 0.09523810)
## 40) texture_mean>=2.967697 19 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.967697 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.473552 724 361 M (0.50138122 0.49861878)
## 42) smoothness_mean>=-2.425205 646 305 M (0.52786378 0.47213622)
## 84) texture_worst>=4.896309 113 35 M (0.69026549 0.30973451) *
## 85) texture_worst< 4.896309 533 263 B (0.49343340 0.50656660) *
## 43) smoothness_mean< -2.425205 78 22 B (0.28205128 0.71794872)
## 86) smoothness_mean< -2.441446 49 22 B (0.44897959 0.55102041) *
## 87) smoothness_mean>=-2.441446 29 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -2.232873 36 2 B (0.05555556 0.94444444)
## 22) smoothness_mean< -2.453321 3 1 M (0.66666667 0.33333333)
## 44) texture_mean>=3.037949 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.037949 1 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.453321 33 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.489159 106 23 B (0.21698113 0.78301887)
## 6) symmetry_worst>=-1.667161 27 13 M (0.51851852 0.48148148)
## 12) smoothness_worst< -1.616835 15 1 M (0.93333333 0.06666667)
## 24) texture_mean< 3.135016 14 0 M (1.00000000 0.00000000) *
## 25) texture_mean>=3.135016 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.616835 12 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.667161 79 9 B (0.11392405 0.88607595)
## 14) compactness_se>=-3.613485 21 7 B (0.33333333 0.66666667)
## 28) smoothness_mean>=-2.508983 3 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean< -2.508983 18 4 B (0.22222222 0.77777778)
## 58) texture_mean>=3.076827 5 1 M (0.80000000 0.20000000)
## 116) texture_mean< 3.103494 4 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 59) texture_mean< 3.076827 13 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.613485 58 2 B (0.03448276 0.96551724)
## 30) compactness_se< -4.692873 11 2 B (0.18181818 0.81818182)
## 60) compactness_se>=-4.711555 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.711555 9 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.692873 47 0 B (0.00000000 1.00000000) *
##
## $trees[[16]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 399 M (0.56250000 0.43750000)
## 2) compactness_se>=-4.691273 882 370 M (0.58049887 0.41950113)
## 4) smoothness_mean>=-2.332942 448 150 M (0.66517857 0.33482143)
## 8) compactness_se>=-4.032549 358 96 M (0.73184358 0.26815642)
## 16) symmetry_worst>=-2.188127 337 80 M (0.76261128 0.23738872)
## 32) smoothness_mean< -2.106197 314 64 M (0.79617834 0.20382166)
## 64) texture_worst>=4.895983 58 0 M (1.00000000 0.00000000) *
## 65) texture_worst< 4.895983 256 64 M (0.75000000 0.25000000) *
## 33) smoothness_mean>=-2.106197 23 7 B (0.30434783 0.69565217)
## 66) symmetry_worst>=-1.596878 8 1 M (0.87500000 0.12500000) *
## 67) symmetry_worst< -1.596878 15 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.188127 21 5 B (0.23809524 0.76190476)
## 34) smoothness_mean>=-2.244441 5 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.244441 16 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.032549 90 36 B (0.40000000 0.60000000)
## 18) smoothness_mean< -2.291157 40 6 M (0.85000000 0.15000000)
## 36) texture_mean>=2.834088 37 3 M (0.91891892 0.08108108)
## 72) compactness_se< -4.098353 36 2 M (0.94444444 0.05555556) *
## 73) compactness_se>=-4.098353 1 0 B (0.00000000 1.00000000) *
## 37) texture_mean< 2.834088 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.291157 50 2 B (0.04000000 0.96000000)
## 38) smoothness_mean>=-2.21595 12 2 B (0.16666667 0.83333333)
## 76) compactness_se< -4.208747 2 0 M (1.00000000 0.00000000) *
## 77) compactness_se>=-4.208747 10 0 B (0.00000000 1.00000000) *
## 39) smoothness_mean< -2.21595 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.332942 434 214 B (0.49308756 0.50691244)
## 10) texture_mean< 3.227241 376 171 M (0.54521277 0.45478723)
## 20) compactness_se< -3.426516 304 118 M (0.61184211 0.38815789)
## 40) texture_mean>=2.874407 247 79 M (0.68016194 0.31983806)
## 80) compactness_se< -4.039628 134 23 M (0.82835821 0.17164179) *
## 81) compactness_se>=-4.039628 113 56 M (0.50442478 0.49557522) *
## 41) texture_mean< 2.874407 57 18 B (0.31578947 0.68421053)
## 82) smoothness_worst>=-1.454595 8 1 M (0.87500000 0.12500000) *
## 83) smoothness_worst< -1.454595 49 11 B (0.22448980 0.77551020) *
## 21) compactness_se>=-3.426516 72 19 B (0.26388889 0.73611111)
## 42) texture_mean>=3.06339 26 10 M (0.61538462 0.38461538)
## 84) symmetry_worst>=-2.189138 20 4 M (0.80000000 0.20000000) *
## 85) symmetry_worst< -2.189138 6 0 B (0.00000000 1.00000000) *
## 43) texture_mean< 3.06339 46 3 B (0.06521739 0.93478261)
## 86) compactness_se< -3.392487 6 2 B (0.33333333 0.66666667) *
## 87) compactness_se>=-3.392487 40 1 B (0.02500000 0.97500000) *
## 11) texture_mean>=3.227241 58 9 B (0.15517241 0.84482759)
## 22) compactness_se>=-3.482708 6 2 M (0.66666667 0.33333333)
## 44) texture_mean>=3.256167 4 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.256167 2 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.482708 52 5 B (0.09615385 0.90384615)
## 46) texture_mean>=3.431382 2 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.431382 50 3 B (0.06000000 0.94000000)
## 94) texture_mean>=3.388429 6 2 B (0.33333333 0.66666667) *
## 95) texture_mean< 3.388429 44 1 B (0.02272727 0.97727273) *
## 3) compactness_se< -4.691273 30 1 B (0.03333333 0.96666667)
## 6) symmetry_worst>=-1.124659 1 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.124659 29 0 B (0.00000000 1.00000000) *
##
## $trees[[17]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) smoothness_mean>=-2.423737 712 329 M (0.53792135 0.46207865)
## 4) compactness_se>=-3.011681 66 11 M (0.83333333 0.16666667)
## 8) smoothness_worst< -1.454202 48 1 M (0.97916667 0.02083333)
## 16) smoothness_mean>=-2.336585 43 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.336585 5 1 M (0.80000000 0.20000000)
## 34) texture_mean>=2.972459 4 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 2.972459 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.454202 18 8 B (0.44444444 0.55555556)
## 18) smoothness_mean>=-2.161865 6 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean< -2.161865 12 2 B (0.16666667 0.83333333)
## 38) symmetry_worst< -1.642275 2 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst>=-1.642275 10 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.011681 646 318 M (0.50773994 0.49226006)
## 10) smoothness_worst>=-1.472307 191 67 M (0.64921466 0.35078534)
## 20) smoothness_mean< -2.300091 57 5 M (0.91228070 0.08771930)
## 40) texture_mean>=2.735974 54 2 M (0.96296296 0.03703704)
## 80) compactness_se>=-4.497673 53 1 M (0.98113208 0.01886792) *
## 81) compactness_se< -4.497673 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.735974 3 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.300091 134 62 M (0.53731343 0.46268657)
## 42) compactness_se>=-4.030558 98 30 M (0.69387755 0.30612245)
## 84) texture_mean>=2.915043 60 7 M (0.88333333 0.11666667) *
## 85) texture_mean< 2.915043 38 15 B (0.39473684 0.60526316) *
## 43) compactness_se< -4.030558 36 4 B (0.11111111 0.88888889)
## 86) symmetry_worst< -1.743442 7 3 M (0.57142857 0.42857143) *
## 87) symmetry_worst>=-1.743442 29 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.472307 455 204 B (0.44835165 0.55164835)
## 22) symmetry_worst< -1.692015 327 150 M (0.54128440 0.45871560)
## 44) smoothness_worst< -1.474843 302 126 M (0.58278146 0.41721854)
## 88) texture_mean< 3.36829 288 112 M (0.61111111 0.38888889) *
## 89) texture_mean>=3.36829 14 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.474843 25 1 B (0.04000000 0.96000000)
## 90) texture_mean>=2.978826 1 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.978826 24 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.692015 128 27 B (0.21093750 0.78906250)
## 46) compactness_se>=-3.4704 20 8 M (0.60000000 0.40000000)
## 92) smoothness_worst>=-1.51165 11 1 M (0.90909091 0.09090909) *
## 93) smoothness_worst< -1.51165 9 2 B (0.22222222 0.77777778) *
## 47) compactness_se< -3.4704 108 15 B (0.13888889 0.86111111)
## 94) texture_worst>=4.818867 3 0 M (1.00000000 0.00000000) *
## 95) texture_worst< 4.818867 105 12 B (0.11428571 0.88571429) *
## 3) smoothness_mean< -2.423737 200 65 B (0.32500000 0.67500000)
## 6) texture_mean< 3.176386 164 65 B (0.39634146 0.60365854)
## 12) smoothness_worst>=-1.656234 126 63 M (0.50000000 0.50000000)
## 24) texture_mean>=3.111958 16 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.111958 110 47 B (0.42727273 0.57272727)
## 50) smoothness_worst< -1.551775 86 39 M (0.54651163 0.45348837)
## 100) smoothness_mean< -2.432353 76 29 M (0.61842105 0.38157895) *
## 101) smoothness_mean>=-2.432353 10 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.551775 24 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.656234 38 2 B (0.05263158 0.94736842)
## 26) smoothness_worst< -1.720903 7 2 B (0.28571429 0.71428571)
## 52) compactness_se>=-3.013033 2 0 M (1.00000000 0.00000000) *
## 53) compactness_se< -3.013033 5 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.720903 31 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=3.176386 36 0 B (0.00000000 1.00000000) *
##
## $trees[[18]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 401 B (0.43969298 0.56030702)
## 2) smoothness_worst>=-1.603315 769 369 B (0.47984395 0.52015605)
## 4) texture_mean>=2.709047 735 367 B (0.49931973 0.50068027)
## 8) compactness_se>=-3.719548 299 118 M (0.60535117 0.39464883)
## 16) texture_mean< 2.746628 28 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=2.746628 271 118 M (0.56457565 0.43542435)
## 34) symmetry_worst>=-1.606092 73 17 M (0.76712329 0.23287671)
## 68) smoothness_mean>=-2.298098 50 5 M (0.90000000 0.10000000) *
## 69) smoothness_mean< -2.298098 23 11 B (0.47826087 0.52173913) *
## 35) symmetry_worst< -1.606092 198 97 B (0.48989899 0.51010101)
## 70) smoothness_mean< -2.14559 180 83 M (0.53888889 0.46111111) *
## 71) smoothness_mean>=-2.14559 18 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.719548 436 186 B (0.42660550 0.57339450)
## 18) compactness_se< -3.859436 366 178 B (0.48633880 0.51366120)
## 36) smoothness_mean< -2.291157 278 122 M (0.56115108 0.43884892)
## 72) smoothness_worst>=-1.55307 192 62 M (0.67708333 0.32291667) *
## 73) smoothness_worst< -1.55307 86 26 B (0.30232558 0.69767442) *
## 37) smoothness_mean>=-2.291157 88 22 B (0.25000000 0.75000000)
## 74) compactness_se>=-4.032549 37 15 M (0.59459459 0.40540541) *
## 75) compactness_se< -4.032549 51 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-3.859436 70 8 B (0.11428571 0.88571429)
## 38) smoothness_worst>=-1.472895 9 3 M (0.66666667 0.33333333)
## 76) smoothness_mean< -2.17953 6 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean>=-2.17953 3 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst< -1.472895 61 2 B (0.03278689 0.96721311)
## 78) symmetry_worst< -1.901985 8 2 B (0.25000000 0.75000000) *
## 79) symmetry_worst>=-1.901985 53 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.709047 34 2 B (0.05882353 0.94117647)
## 10) texture_worst< 3.80118 10 2 B (0.20000000 0.80000000)
## 20) texture_mean>=2.630644 2 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.630644 8 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=3.80118 24 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.603315 143 32 B (0.22377622 0.77622378)
## 6) symmetry_worst>=-1.868413 63 28 B (0.44444444 0.55555556)
## 12) texture_worst>=4.334485 50 22 M (0.56000000 0.44000000)
## 24) texture_mean< 3.062357 33 7 M (0.78787879 0.21212121)
## 48) texture_mean>=2.939162 28 2 M (0.92857143 0.07142857)
## 96) compactness_se>=-4.938351 27 1 M (0.96296296 0.03703704) *
## 97) compactness_se< -4.938351 1 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.939162 5 0 B (0.00000000 1.00000000) *
## 25) texture_mean>=3.062357 17 2 B (0.11764706 0.88235294)
## 50) smoothness_mean>=-2.337942 2 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.337942 15 0 B (0.00000000 1.00000000) *
## 13) texture_worst< 4.334485 13 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.868413 80 4 B (0.05000000 0.95000000)
## 14) smoothness_mean>=-2.373736 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.373736 79 3 B (0.03797468 0.96202532)
## 30) smoothness_worst< -1.718904 10 3 B (0.30000000 0.70000000)
## 60) compactness_se>=-3.013033 3 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -3.013033 7 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.718904 69 0 B (0.00000000 1.00000000) *
##
## $trees[[19]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 353 B (0.38706140 0.61293860)
## 2) symmetry_worst< -2.385442 27 3 M (0.88888889 0.11111111)
## 4) texture_mean< 3.283931 26 2 M (0.92307692 0.07692308)
## 8) smoothness_worst< -1.534654 21 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst>=-1.534654 5 2 M (0.60000000 0.40000000)
## 18) texture_mean>=3.050671 3 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.050671 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.283931 1 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.385442 885 329 B (0.37175141 0.62824859)
## 6) symmetry_worst>=-1.840831 498 217 B (0.43574297 0.56425703)
## 12) symmetry_worst< -1.750623 159 70 M (0.55974843 0.44025157)
## 24) smoothness_worst>=-1.547262 101 30 M (0.70297030 0.29702970)
## 48) smoothness_mean< -2.210016 85 15 M (0.82352941 0.17647059)
## 96) compactness_se>=-4.388189 81 11 M (0.86419753 0.13580247) *
## 97) compactness_se< -4.388189 4 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.210016 16 1 B (0.06250000 0.93750000)
## 98) compactness_se>=-3.317826 1 0 M (1.00000000 0.00000000) *
## 99) compactness_se< -3.317826 15 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.547262 58 18 B (0.31034483 0.68965517)
## 50) texture_worst>=4.56463 12 1 M (0.91666667 0.08333333)
## 100) texture_mean< 3.176386 11 0 M (1.00000000 0.00000000) *
## 101) texture_mean>=3.176386 1 0 B (0.00000000 1.00000000) *
## 51) texture_worst< 4.56463 46 7 B (0.15217391 0.84782609)
## 102) smoothness_mean>=-2.302636 4 0 M (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.302636 42 3 B (0.07142857 0.92857143) *
## 13) symmetry_worst>=-1.750623 339 128 B (0.37758112 0.62241888)
## 26) smoothness_worst>=-1.473088 100 41 M (0.59000000 0.41000000)
## 52) compactness_se< -2.961809 85 28 M (0.67058824 0.32941176)
## 104) symmetry_worst>=-1.65458 67 14 M (0.79104478 0.20895522) *
## 105) symmetry_worst< -1.65458 18 4 B (0.22222222 0.77777778) *
## 53) compactness_se>=-2.961809 15 2 B (0.13333333 0.86666667)
## 106) texture_mean< 2.81718 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean>=2.81718 14 1 B (0.07142857 0.92857143) *
## 27) smoothness_worst< -1.473088 239 69 B (0.28870293 0.71129707)
## 54) compactness_se>=-3.483184 52 20 M (0.61538462 0.38461538)
## 108) texture_mean>=2.956366 37 7 M (0.81081081 0.18918919) *
## 109) texture_mean< 2.956366 15 2 B (0.13333333 0.86666667) *
## 55) compactness_se< -3.483184 187 37 B (0.19786096 0.80213904)
## 110) compactness_se< -4.658767 5 0 M (1.00000000 0.00000000) *
## 111) compactness_se>=-4.658767 182 32 B (0.17582418 0.82417582) *
## 7) symmetry_worst< -1.840831 387 112 B (0.28940568 0.71059432)
## 14) texture_worst>=4.907333 79 34 M (0.56962025 0.43037975)
## 28) symmetry_worst>=-2.207988 54 12 M (0.77777778 0.22222222)
## 56) smoothness_mean>=-2.427815 34 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.427815 20 8 B (0.40000000 0.60000000)
## 114) texture_worst< 4.987149 8 0 M (1.00000000 0.00000000) *
## 115) texture_worst>=4.987149 12 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -2.207988 25 3 B (0.12000000 0.88000000)
## 58) compactness_se>=-3.413706 3 0 M (1.00000000 0.00000000) *
## 59) compactness_se< -3.413706 22 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.907333 308 67 B (0.21753247 0.78246753)
## 30) compactness_se>=-3.02233 20 7 M (0.65000000 0.35000000)
## 60) compactness_se< -2.870592 12 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-2.870592 8 1 B (0.12500000 0.87500000)
## 122) texture_mean>=3.109826 1 0 M (1.00000000 0.00000000) *
## 123) texture_mean< 3.109826 7 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.02233 288 54 B (0.18750000 0.81250000)
## 62) smoothness_mean< -2.35264 140 44 B (0.31428571 0.68571429)
## 124) smoothness_mean>=-2.394871 44 13 M (0.70454545 0.29545455) *
## 125) smoothness_mean< -2.394871 96 13 B (0.13541667 0.86458333) *
## 63) smoothness_mean>=-2.35264 148 10 B (0.06756757 0.93243243)
## 126) smoothness_worst>=-1.49704 73 10 B (0.13698630 0.86301370) *
## 127) smoothness_worst< -1.49704 75 0 B (0.00000000 1.00000000) *
##
## $trees[[20]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 403 B (0.44188596 0.55811404)
## 2) symmetry_worst>=-1.353976 49 10 M (0.79591837 0.20408163)
## 4) texture_mean>=2.644674 46 7 M (0.84782609 0.15217391)
## 8) compactness_se< -2.588521 41 3 M (0.92682927 0.07317073)
## 16) texture_mean< 3.104075 34 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.104075 7 3 M (0.57142857 0.42857143)
## 34) texture_mean>=3.116842 4 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.116842 3 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.588521 5 1 B (0.20000000 0.80000000)
## 18) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.915767 4 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.644674 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.353976 863 364 B (0.42178447 0.57821553)
## 6) compactness_se< -3.355844 757 343 B (0.45310436 0.54689564)
## 12) compactness_se>=-3.721197 239 94 M (0.60669456 0.39330544)
## 24) texture_worst< 4.616724 154 41 M (0.73376623 0.26623377)
## 48) smoothness_mean>=-2.322902 77 6 M (0.92207792 0.07792208)
## 96) texture_worst>=4.050785 65 1 M (0.98461538 0.01538462) *
## 97) texture_worst< 4.050785 12 5 M (0.58333333 0.41666667) *
## 49) smoothness_mean< -2.322902 77 35 M (0.54545455 0.45454545)
## 98) texture_worst>=4.56463 20 0 M (1.00000000 0.00000000) *
## 99) texture_worst< 4.56463 57 22 B (0.38596491 0.61403509) *
## 25) texture_worst>=4.616724 85 32 B (0.37647059 0.62352941)
## 50) smoothness_mean>=-2.286719 19 4 M (0.78947368 0.21052632)
## 100) smoothness_mean< -2.119611 15 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean>=-2.119611 4 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.286719 66 17 B (0.25757576 0.74242424)
## 102) smoothness_mean< -2.349943 30 15 M (0.50000000 0.50000000) *
## 103) smoothness_mean>=-2.349943 36 2 B (0.05555556 0.94444444) *
## 13) compactness_se< -3.721197 518 198 B (0.38223938 0.61776062)
## 26) compactness_se>=-4.705565 488 198 B (0.40573770 0.59426230)
## 52) compactness_se< -4.116284 249 118 M (0.52610442 0.47389558)
## 104) texture_mean< 3.032503 168 61 M (0.63690476 0.36309524) *
## 105) texture_mean>=3.032503 81 24 B (0.29629630 0.70370370) *
## 53) compactness_se>=-4.116284 239 67 B (0.28033473 0.71966527)
## 106) texture_worst>=4.895983 46 17 M (0.63043478 0.36956522) *
## 107) texture_worst< 4.895983 193 38 B (0.19689119 0.80310881) *
## 27) compactness_se< -4.705565 30 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.355844 106 21 B (0.19811321 0.80188679)
## 14) texture_mean>=3.038537 33 16 B (0.48484848 0.51515152)
## 28) texture_mean< 3.216873 20 6 M (0.70000000 0.30000000)
## 56) smoothness_mean< -2.154474 17 3 M (0.82352941 0.17647059)
## 112) symmetry_worst>=-2.143533 13 0 M (1.00000000 0.00000000) *
## 113) symmetry_worst< -2.143533 4 1 B (0.25000000 0.75000000) *
## 57) smoothness_mean>=-2.154474 3 0 B (0.00000000 1.00000000) *
## 29) texture_mean>=3.216873 13 2 B (0.15384615 0.84615385)
## 58) texture_mean>=3.252756 2 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.252756 11 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.038537 73 5 B (0.06849315 0.93150685)
## 30) smoothness_mean>=-2.082188 3 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.082188 70 2 B (0.02857143 0.97142857)
## 62) smoothness_worst>=-1.481325 15 2 B (0.13333333 0.86666667)
## 124) texture_mean>=3.031099 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.031099 14 1 B (0.07142857 0.92857143) *
## 63) smoothness_worst< -1.481325 55 0 B (0.00000000 1.00000000) *
##
## $trees[[21]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 387 B (0.42434211 0.57565789)
## 2) symmetry_worst>=-1.366937 66 10 M (0.84848485 0.15151515)
## 4) smoothness_mean< -2.036051 64 8 M (0.87500000 0.12500000)
## 8) compactness_se< -2.588521 61 6 M (0.90163934 0.09836066)
## 16) texture_mean< 3.104075 50 2 M (0.96000000 0.04000000)
## 32) smoothness_mean>=-2.352085 34 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.352085 16 2 M (0.87500000 0.12500000)
## 66) texture_mean>=2.89093 14 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.89093 2 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.104075 11 4 M (0.63636364 0.36363636)
## 34) texture_mean>=3.116842 7 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.116842 4 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.588521 3 1 B (0.33333333 0.66666667)
## 18) texture_mean>=2.996569 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.996569 2 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.036051 2 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 846 331 B (0.39125296 0.60874704)
## 6) texture_mean>=3.388429 17 2 M (0.88235294 0.11764706)
## 12) compactness_se>=-4.317414 15 0 M (1.00000000 0.00000000) *
## 13) compactness_se< -4.317414 2 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 3.388429 829 316 B (0.38118215 0.61881785)
## 14) smoothness_mean>=-2.423454 627 265 B (0.42264753 0.57735247)
## 28) symmetry_worst>=-1.786753 307 153 M (0.50162866 0.49837134)
## 56) symmetry_worst< -1.781339 23 0 M (1.00000000 0.00000000) *
## 57) symmetry_worst>=-1.781339 284 131 B (0.46126761 0.53873239)
## 114) compactness_se>=-3.703794 110 42 M (0.61818182 0.38181818) *
## 115) compactness_se< -3.703794 174 63 B (0.36206897 0.63793103) *
## 29) symmetry_worst< -1.786753 320 111 B (0.34687500 0.65312500)
## 58) symmetry_worst< -1.938643 177 78 B (0.44067797 0.55932203)
## 116) symmetry_worst>=-1.964873 37 6 M (0.83783784 0.16216216) *
## 117) symmetry_worst< -1.964873 140 47 B (0.33571429 0.66428571) *
## 59) symmetry_worst>=-1.938643 143 33 B (0.23076923 0.76923077)
## 118) texture_mean< 2.724206 14 4 M (0.71428571 0.28571429) *
## 119) texture_mean>=2.724206 129 23 B (0.17829457 0.82170543) *
## 15) smoothness_mean< -2.423454 202 51 B (0.25247525 0.74752475)
## 30) symmetry_worst>=-1.541072 25 8 M (0.68000000 0.32000000)
## 60) smoothness_mean< -2.431217 21 4 M (0.80952381 0.19047619)
## 120) texture_mean>=2.973641 10 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.973641 11 4 M (0.63636364 0.36363636) *
## 61) smoothness_mean>=-2.431217 4 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.541072 177 34 B (0.19209040 0.80790960)
## 62) smoothness_mean< -2.467991 111 30 B (0.27027027 0.72972973)
## 124) smoothness_mean>=-2.468227 7 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.468227 104 23 B (0.22115385 0.77884615) *
## 63) smoothness_mean>=-2.467991 66 4 B (0.06060606 0.93939394)
## 126) symmetry_worst< -2.004084 19 4 B (0.21052632 0.78947368) *
## 127) symmetry_worst>=-2.004084 47 0 B (0.00000000 1.00000000) *
##
## $trees[[22]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 B (0.47587719 0.52412281)
## 2) texture_mean>=2.708379 867 426 B (0.49134948 0.50865052)
## 4) smoothness_worst>=-1.501069 363 155 M (0.57300275 0.42699725)
## 8) smoothness_worst< -1.476605 142 32 M (0.77464789 0.22535211)
## 16) smoothness_worst>=-1.482699 75 6 M (0.92000000 0.08000000)
## 32) texture_worst>=4.126187 73 4 M (0.94520548 0.05479452)
## 64) texture_worst< 4.635614 63 0 M (1.00000000 0.00000000) *
## 65) texture_worst>=4.635614 10 4 M (0.60000000 0.40000000) *
## 33) texture_worst< 4.126187 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.482699 67 26 M (0.61194030 0.38805970)
## 34) smoothness_worst< -1.484675 57 16 M (0.71929825 0.28070175)
## 68) texture_worst>=4.484566 48 8 M (0.83333333 0.16666667) *
## 69) texture_worst< 4.484566 9 1 B (0.11111111 0.88888889) *
## 35) smoothness_worst>=-1.484675 10 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.476605 221 98 B (0.44343891 0.55656109)
## 18) smoothness_worst>=-1.473476 192 95 B (0.49479167 0.50520833)
## 36) compactness_se>=-4.032549 128 51 M (0.60156250 0.39843750)
## 72) compactness_se< -3.532908 60 8 M (0.86666667 0.13333333) *
## 73) compactness_se>=-3.532908 68 25 B (0.36764706 0.63235294) *
## 37) compactness_se< -4.032549 64 18 B (0.28125000 0.71875000)
## 74) texture_mean>=3.07984 14 5 M (0.64285714 0.35714286) *
## 75) texture_mean< 3.07984 50 9 B (0.18000000 0.82000000) *
## 19) smoothness_worst< -1.473476 29 3 B (0.10344828 0.89655172)
## 38) texture_mean>=3.069079 3 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 3.069079 26 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.501069 504 218 B (0.43253968 0.56746032)
## 10) smoothness_worst< -1.519464 451 210 B (0.46563193 0.53436807)
## 20) smoothness_worst>=-1.520292 20 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.520292 431 190 B (0.44083527 0.55916473)
## 42) smoothness_worst< -1.533868 375 180 B (0.48000000 0.52000000)
## 84) texture_worst>=5.093455 43 10 M (0.76744186 0.23255814) *
## 85) texture_worst< 5.093455 332 147 B (0.44277108 0.55722892) *
## 43) smoothness_worst>=-1.533868 56 10 B (0.17857143 0.82142857)
## 86) texture_mean>=3.065024 20 9 B (0.45000000 0.55000000) *
## 87) texture_mean< 3.065024 36 1 B (0.02777778 0.97222222) *
## 11) smoothness_worst>=-1.519464 53 8 B (0.15094340 0.84905660)
## 22) texture_mean>=3.006423 25 8 B (0.32000000 0.68000000)
## 44) symmetry_worst< -1.551134 9 2 M (0.77777778 0.22222222)
## 88) texture_worst>=4.577291 7 0 M (1.00000000 0.00000000) *
## 89) texture_worst< 4.577291 2 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.551134 16 1 B (0.06250000 0.93750000)
## 90) smoothness_mean>=-2.263106 1 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.263106 15 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.006423 28 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.708379 45 8 B (0.17777778 0.82222222)
## 6) texture_mean< 2.479051 6 1 M (0.83333333 0.16666667)
## 12) smoothness_mean>=-2.170026 5 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.170026 1 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=2.479051 39 3 B (0.07692308 0.92307692)
## 14) symmetry_worst>=-1.112025 2 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst< -1.112025 37 1 B (0.02702703 0.97297297)
## 30) texture_mean>=2.648549 10 1 B (0.10000000 0.90000000)
## 60) texture_mean< 2.666527 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.666527 9 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 2.648549 27 0 B (0.00000000 1.00000000) *
##
## $trees[[23]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 426 M (0.53289474 0.46710526)
## 2) symmetry_worst>=-1.293329 35 3 M (0.91428571 0.08571429)
## 4) smoothness_worst>=-1.49848 29 1 M (0.96551724 0.03448276)
## 8) smoothness_mean>=-2.340816 28 0 M (1.00000000 0.00000000) *
## 9) smoothness_mean< -2.340816 1 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.49848 6 2 M (0.66666667 0.33333333)
## 10) smoothness_mean< -2.349786 4 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.349786 2 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.293329 877 423 M (0.51767389 0.48232611)
## 6) texture_mean>=2.653549 846 396 M (0.53191489 0.46808511)
## 12) texture_mean< 3.227241 785 354 M (0.54904459 0.45095541)
## 24) texture_worst>=5.032208 40 5 M (0.87500000 0.12500000)
## 48) texture_worst< 5.280287 35 0 M (1.00000000 0.00000000) *
## 49) texture_worst>=5.280287 5 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 5.032208 745 349 M (0.53154362 0.46845638)
## 50) smoothness_worst< -1.403628 702 316 M (0.54985755 0.45014245)
## 100) compactness_se< -3.392487 619 262 M (0.57673667 0.42326333) *
## 101) compactness_se>=-3.392487 83 29 B (0.34939759 0.65060241) *
## 51) smoothness_worst>=-1.403628 43 10 B (0.23255814 0.76744186)
## 102) compactness_se>=-3.217781 12 3 M (0.75000000 0.25000000) *
## 103) compactness_se< -3.217781 31 1 B (0.03225806 0.96774194) *
## 13) texture_mean>=3.227241 61 19 B (0.31147541 0.68852459)
## 26) smoothness_worst< -1.582589 12 2 M (0.83333333 0.16666667)
## 52) texture_mean>=3.331484 10 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 3.331484 2 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.582589 49 9 B (0.18367347 0.81632653)
## 54) compactness_se>=-3.575987 7 2 M (0.71428571 0.28571429)
## 108) compactness_se< -2.831802 5 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-2.831802 2 0 B (0.00000000 1.00000000) *
## 55) compactness_se< -3.575987 42 4 B (0.09523810 0.90476190)
## 110) smoothness_mean>=-2.306298 2 0 M (1.00000000 0.00000000) *
## 111) smoothness_mean< -2.306298 40 2 B (0.05000000 0.95000000) *
## 7) texture_mean< 2.653549 31 4 B (0.12903226 0.87096774)
## 14) smoothness_mean>=-2.07745 7 3 M (0.57142857 0.42857143)
## 28) texture_mean< 2.515298 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.515298 3 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.07745 24 0 B (0.00000000 1.00000000) *
##
## $trees[[24]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 428 B (0.46929825 0.53070175)
## 2) symmetry_worst>=-1.348749 42 6 M (0.85714286 0.14285714)
## 4) texture_mean>=2.756192 36 2 M (0.94444444 0.05555556)
## 8) texture_worst>=4.373597 26 0 M (1.00000000 0.00000000) *
## 9) texture_worst< 4.373597 10 2 M (0.80000000 0.20000000)
## 18) texture_worst< 4.279513 8 0 M (1.00000000 0.00000000) *
## 19) texture_worst>=4.279513 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.756192 6 2 B (0.33333333 0.66666667)
## 10) compactness_se>=-3.3026 2 0 M (1.00000000 0.00000000) *
## 11) compactness_se< -3.3026 4 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.348749 870 392 B (0.45057471 0.54942529)
## 6) compactness_se>=-4.705732 850 392 B (0.46117647 0.53882353)
## 12) texture_worst< 4.54138 369 172 M (0.53387534 0.46612466)
## 24) texture_worst>=4.523593 41 6 M (0.85365854 0.14634146)
## 48) smoothness_mean< -2.234468 31 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.234468 10 4 B (0.40000000 0.60000000)
## 98) texture_mean>=3.023554 4 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.023554 6 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.523593 328 162 B (0.49390244 0.50609756)
## 50) smoothness_worst< -1.541278 152 57 M (0.62500000 0.37500000)
## 100) compactness_se>=-4.501722 126 36 M (0.71428571 0.28571429) *
## 101) compactness_se< -4.501722 26 5 B (0.19230769 0.80769231) *
## 51) smoothness_worst>=-1.541278 176 67 B (0.38068182 0.61931818)
## 102) smoothness_worst>=-1.483493 102 47 M (0.53921569 0.46078431) *
## 103) smoothness_worst< -1.483493 74 12 B (0.16216216 0.83783784) *
## 13) texture_worst>=4.54138 481 195 B (0.40540541 0.59459459)
## 26) compactness_se>=-3.334337 56 16 M (0.71428571 0.28571429)
## 52) smoothness_mean>=-2.41714 37 4 M (0.89189189 0.10810811)
## 104) texture_mean>=3.03709 31 0 M (1.00000000 0.00000000) *
## 105) texture_mean< 3.03709 6 2 B (0.33333333 0.66666667) *
## 53) smoothness_mean< -2.41714 19 7 B (0.36842105 0.63157895)
## 106) compactness_se< -3.106177 7 0 M (1.00000000 0.00000000) *
## 107) compactness_se>=-3.106177 12 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.334337 425 155 B (0.36470588 0.63529412)
## 54) smoothness_mean< -2.351049 235 111 B (0.47234043 0.52765957)
## 108) smoothness_mean>=-2.367605 23 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.367605 212 88 B (0.41509434 0.58490566) *
## 55) smoothness_mean>=-2.351049 190 44 B (0.23157895 0.76842105)
## 110) texture_worst>=5.277564 9 0 M (1.00000000 0.00000000) *
## 111) texture_worst< 5.277564 181 35 B (0.19337017 0.80662983) *
## 7) compactness_se< -4.705732 20 0 B (0.00000000 1.00000000) *
##
## $trees[[25]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_mean>=2.892591 632 263 M (0.58386076 0.41613924)
## 4) compactness_se< -4.094455 216 63 M (0.70833333 0.29166667)
## 8) smoothness_mean>=-2.426508 128 23 M (0.82031250 0.17968750)
## 16) smoothness_mean< -2.295141 113 12 M (0.89380531 0.10619469)
## 32) texture_mean< 3.227241 106 7 M (0.93396226 0.06603774)
## 64) texture_worst>=4.35485 104 5 M (0.95192308 0.04807692) *
## 65) texture_worst< 4.35485 2 0 B (0.00000000 1.00000000) *
## 33) texture_mean>=3.227241 7 2 B (0.28571429 0.71428571)
## 66) compactness_se>=-4.299856 2 0 M (1.00000000 0.00000000) *
## 67) compactness_se< -4.299856 5 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.295141 15 4 B (0.26666667 0.73333333)
## 34) smoothness_mean>=-2.222419 4 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.222419 11 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.426508 88 40 M (0.54545455 0.45454545)
## 18) symmetry_worst>=-1.705164 59 17 M (0.71186441 0.28813559)
## 36) smoothness_worst< -1.567962 37 2 M (0.94594595 0.05405405)
## 72) texture_mean>=2.958609 35 0 M (1.00000000 0.00000000) *
## 73) texture_mean< 2.958609 2 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst>=-1.567962 22 7 B (0.31818182 0.68181818)
## 74) texture_mean< 2.936149 7 0 M (1.00000000 0.00000000) *
## 75) texture_mean>=2.936149 15 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.705164 29 6 B (0.20689655 0.79310345)
## 38) texture_worst>=4.89091 12 6 M (0.50000000 0.50000000)
## 76) texture_worst< 4.984007 6 0 M (1.00000000 0.00000000) *
## 77) texture_worst>=4.984007 6 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 4.89091 17 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-4.094455 416 200 M (0.51923077 0.48076923)
## 10) smoothness_mean>=-2.28279 148 39 M (0.73648649 0.26351351)
## 20) compactness_se>=-4.030876 139 30 M (0.78417266 0.21582734)
## 40) texture_mean>=2.912343 131 24 M (0.81679389 0.18320611)
## 80) smoothness_worst>=-1.500666 86 7 M (0.91860465 0.08139535) *
## 81) smoothness_worst< -1.500666 45 17 M (0.62222222 0.37777778) *
## 41) texture_mean< 2.912343 8 2 B (0.25000000 0.75000000)
## 82) smoothness_mean< -2.17953 2 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.17953 6 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.030876 9 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.28279 268 107 B (0.39925373 0.60074627)
## 22) smoothness_worst< -1.509803 176 88 M (0.50000000 0.50000000)
## 44) texture_worst>=4.415916 161 73 M (0.54658385 0.45341615)
## 88) symmetry_worst>=-1.801537 69 18 M (0.73913043 0.26086957) *
## 89) symmetry_worst< -1.801537 92 37 B (0.40217391 0.59782609) *
## 45) texture_worst< 4.415916 15 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst>=-1.509803 92 19 B (0.20652174 0.79347826)
## 46) texture_worst>=4.890484 26 12 M (0.53846154 0.46153846)
## 92) smoothness_worst>=-1.48132 13 1 M (0.92307692 0.07692308) *
## 93) smoothness_worst< -1.48132 13 2 B (0.15384615 0.84615385) *
## 47) texture_worst< 4.890484 66 5 B (0.07575758 0.92424242)
## 94) smoothness_mean< -2.37669 7 3 M (0.57142857 0.42857143) *
## 95) smoothness_mean>=-2.37669 59 1 B (0.01694915 0.98305085) *
## 3) texture_mean< 2.892591 280 101 B (0.36071429 0.63928571)
## 6) compactness_se>=-4.198706 215 92 B (0.42790698 0.57209302)
## 12) compactness_se< -3.427747 173 86 M (0.50289017 0.49710983)
## 24) compactness_se>=-3.894783 93 34 M (0.63440860 0.36559140)
## 48) texture_worst>=4.250385 32 4 M (0.87500000 0.12500000)
## 96) smoothness_mean>=-2.358315 28 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.358315 4 0 B (0.00000000 1.00000000) *
## 49) texture_worst< 4.250385 61 30 M (0.50819672 0.49180328)
## 98) texture_worst< 3.895613 26 4 M (0.84615385 0.15384615) *
## 99) texture_worst>=3.895613 35 9 B (0.25714286 0.74285714) *
## 25) compactness_se< -3.894783 80 28 B (0.35000000 0.65000000)
## 50) compactness_se< -4.160164 22 3 M (0.86363636 0.13636364)
## 100) texture_mean>=2.779034 19 0 M (1.00000000 0.00000000) *
## 101) texture_mean< 2.779034 3 0 B (0.00000000 1.00000000) *
## 51) compactness_se>=-4.160164 58 9 B (0.15517241 0.84482759)
## 102) smoothness_worst>=-1.451541 11 2 M (0.81818182 0.18181818) *
## 103) smoothness_worst< -1.451541 47 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.427747 42 5 B (0.11904762 0.88095238)
## 26) smoothness_mean>=-2.069166 4 1 M (0.75000000 0.25000000)
## 52) texture_mean>=2.65428 3 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.65428 1 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.069166 38 2 B (0.05263158 0.94736842)
## 54) symmetry_worst>=-1.316602 3 1 M (0.66666667 0.33333333)
## 108) texture_mean< 2.830318 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.830318 1 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.316602 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -4.198706 65 9 B (0.13846154 0.86153846)
## 14) texture_mean>=2.87384 10 4 M (0.60000000 0.40000000)
## 28) texture_mean< 2.884497 6 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.884497 4 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 2.87384 55 3 B (0.05454545 0.94545455)
## 30) texture_worst>=4.600092 13 3 B (0.23076923 0.76923077)
## 60) smoothness_mean< -2.330887 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.330887 10 0 B (0.00000000 1.00000000) *
## 31) texture_worst< 4.600092 42 0 B (0.00000000 1.00000000) *
##
## $trees[[26]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) texture_mean>=2.960364 515 227 M (0.55922330 0.44077670)
## 4) texture_worst>=4.354728 499 211 M (0.57715431 0.42284569)
## 8) texture_worst< 4.644679 159 43 M (0.72955975 0.27044025)
## 16) smoothness_worst>=-1.498254 48 2 M (0.95833333 0.04166667)
## 32) compactness_se>=-4.35833 47 1 M (0.97872340 0.02127660)
## 64) symmetry_worst>=-1.833099 42 0 M (1.00000000 0.00000000) *
## 65) symmetry_worst< -1.833099 5 1 M (0.80000000 0.20000000) *
## 33) compactness_se< -4.35833 1 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.498254 111 41 M (0.63063063 0.36936937)
## 34) smoothness_mean< -2.229408 100 30 M (0.70000000 0.30000000)
## 68) compactness_se< -2.82386 91 21 M (0.76923077 0.23076923) *
## 69) compactness_se>=-2.82386 9 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.229408 11 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.644679 340 168 M (0.50588235 0.49411765)
## 18) texture_worst>=5.016194 100 27 M (0.73000000 0.27000000)
## 36) symmetry_worst>=-2.063111 77 9 M (0.88311688 0.11688312)
## 72) smoothness_mean>=-2.58821 76 8 M (0.89473684 0.10526316) *
## 73) smoothness_mean< -2.58821 1 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -2.063111 23 5 B (0.21739130 0.78260870)
## 74) compactness_se>=-3.413706 5 0 M (1.00000000 0.00000000) *
## 75) compactness_se< -3.413706 18 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 5.016194 240 99 B (0.41250000 0.58750000)
## 38) compactness_se< -4.436859 32 7 M (0.78125000 0.21875000)
## 76) compactness_se>=-4.590265 25 1 M (0.96000000 0.04000000) *
## 77) compactness_se< -4.590265 7 1 B (0.14285714 0.85714286) *
## 39) compactness_se>=-4.436859 208 74 B (0.35576923 0.64423077)
## 78) symmetry_worst< -2.000522 50 18 M (0.64000000 0.36000000) *
## 79) symmetry_worst>=-2.000522 158 42 B (0.26582278 0.73417722) *
## 5) texture_worst< 4.354728 16 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.960364 397 148 B (0.37279597 0.62720403)
## 6) texture_mean< 2.948902 363 148 B (0.40771350 0.59228650)
## 12) texture_mean>=2.927988 56 17 M (0.69642857 0.30357143)
## 24) compactness_se>=-4.177518 51 12 M (0.76470588 0.23529412)
## 48) texture_mean< 2.938103 23 0 M (1.00000000 0.00000000) *
## 49) texture_mean>=2.938103 28 12 M (0.57142857 0.42857143)
## 98) texture_mean>=2.947329 12 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.947329 16 4 B (0.25000000 0.75000000) *
## 25) compactness_se< -4.177518 5 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.927988 307 109 B (0.35504886 0.64495114)
## 26) smoothness_worst< -1.539792 95 47 M (0.50526316 0.49473684)
## 52) smoothness_worst>=-1.547262 18 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.547262 77 30 B (0.38961039 0.61038961)
## 106) smoothness_mean< -2.436819 41 18 M (0.56097561 0.43902439) *
## 107) smoothness_mean>=-2.436819 36 7 B (0.19444444 0.80555556) *
## 27) smoothness_worst>=-1.539792 212 61 B (0.28773585 0.71226415)
## 54) compactness_se>=-4.198706 166 61 B (0.36746988 0.63253012)
## 108) texture_mean< 2.893423 137 60 B (0.43795620 0.56204380) *
## 109) texture_mean>=2.893423 29 1 B (0.03448276 0.96551724) *
## 55) compactness_se< -4.198706 46 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=2.948902 34 0 B (0.00000000 1.00000000) *
##
## $trees[[27]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 422 M (0.53728070 0.46271930)
## 2) texture_mean>=2.709047 873 389 M (0.55441008 0.44558992)
## 4) compactness_se>=-3.721197 350 125 M (0.64285714 0.35714286)
## 8) compactness_se< -3.57681 68 6 M (0.91176471 0.08823529)
## 16) smoothness_mean>=-2.509667 66 4 M (0.93939394 0.06060606)
## 32) texture_worst< 4.855419 54 0 M (1.00000000 0.00000000) *
## 33) texture_worst>=4.855419 12 4 M (0.66666667 0.33333333)
## 66) texture_mean>=3.340739 8 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.340739 4 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.509667 2 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-3.57681 282 119 M (0.57801418 0.42198582)
## 18) smoothness_worst>=-1.618016 251 94 M (0.62549801 0.37450199)
## 36) texture_mean>=3.058688 84 16 M (0.80952381 0.19047619)
## 72) smoothness_worst< -1.468038 68 5 M (0.92647059 0.07352941) *
## 73) smoothness_worst>=-1.468038 16 5 B (0.31250000 0.68750000) *
## 37) texture_mean< 3.058688 167 78 M (0.53293413 0.46706587)
## 74) texture_mean< 3.001714 111 32 M (0.71171171 0.28828829) *
## 75) texture_mean>=3.001714 56 10 B (0.17857143 0.82142857) *
## 19) smoothness_worst< -1.618016 31 6 B (0.19354839 0.80645161)
## 38) smoothness_worst< -1.694287 5 0 M (1.00000000 0.00000000) *
## 39) smoothness_worst>=-1.694287 26 1 B (0.03846154 0.96153846)
## 78) texture_mean>=3.166164 1 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.166164 25 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.721197 523 259 B (0.49521989 0.50478011)
## 10) smoothness_worst>=-1.48191 161 57 M (0.64596273 0.35403727)
## 20) compactness_se>=-4.510489 148 44 M (0.70270270 0.29729730)
## 40) smoothness_mean< -2.235394 122 27 M (0.77868852 0.22131148)
## 80) texture_worst< 4.550742 66 4 M (0.93939394 0.06060606) *
## 81) texture_worst>=4.550742 56 23 M (0.58928571 0.41071429) *
## 41) smoothness_mean>=-2.235394 26 9 B (0.34615385 0.65384615)
## 82) texture_mean>=2.939917 12 3 M (0.75000000 0.25000000) *
## 83) texture_mean< 2.939917 14 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.510489 13 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.48191 362 155 B (0.42817680 0.57182320)
## 22) compactness_se< -3.869459 313 155 B (0.49520767 0.50479233)
## 44) smoothness_mean< -2.294121 287 132 M (0.54006969 0.45993031)
## 88) texture_mean>=2.892314 224 83 M (0.62946429 0.37053571) *
## 89) texture_mean< 2.892314 63 14 B (0.22222222 0.77777778) *
## 45) smoothness_mean>=-2.294121 26 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-3.869459 49 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.709047 39 6 B (0.15384615 0.84615385)
## 6) texture_worst< 3.858337 14 6 B (0.42857143 0.57142857)
## 12) compactness_se>=-3.808227 8 2 M (0.75000000 0.25000000)
## 24) smoothness_mean< -1.942706 6 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean>=-1.942706 2 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -3.808227 6 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=3.858337 25 0 B (0.00000000 1.00000000) *
##
## $trees[[28]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 B (0.48793860 0.51206140)
## 2) texture_mean>=3.058002 289 116 M (0.59861592 0.40138408)
## 4) smoothness_worst>=-1.603555 234 75 M (0.67948718 0.32051282)
## 8) texture_worst< 4.797934 78 5 M (0.93589744 0.06410256)
## 16) texture_worst>=4.496164 74 1 M (0.98648649 0.01351351)
## 32) smoothness_worst< -1.469988 73 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst>=-1.469988 1 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.496164 4 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.797934 156 70 M (0.55128205 0.44871795)
## 18) smoothness_mean>=-2.501158 141 55 M (0.60992908 0.39007092)
## 36) texture_worst>=4.897936 116 36 M (0.68965517 0.31034483)
## 72) texture_mean>=3.082368 108 29 M (0.73148148 0.26851852) *
## 73) texture_mean< 3.082368 8 1 B (0.12500000 0.87500000) *
## 37) texture_worst< 4.897936 25 6 B (0.24000000 0.76000000)
## 74) texture_mean< 3.09883 6 1 M (0.83333333 0.16666667) *
## 75) texture_mean>=3.09883 19 1 B (0.05263158 0.94736842) *
## 19) smoothness_mean< -2.501158 15 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.603555 55 14 B (0.25454545 0.74545455)
## 10) compactness_se>=-3.013033 9 1 M (0.88888889 0.11111111)
## 20) texture_mean>=3.076827 8 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.076827 1 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.013033 46 6 B (0.13043478 0.86956522)
## 22) compactness_se< -4.507137 10 4 M (0.60000000 0.40000000)
## 44) compactness_se>=-4.572499 6 0 M (1.00000000 0.00000000) *
## 45) compactness_se< -4.572499 4 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-4.507137 36 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.058002 623 272 B (0.43659711 0.56340289)
## 6) symmetry_worst>=-1.325507 30 4 M (0.86666667 0.13333333)
## 12) compactness_se< -2.588521 27 2 M (0.92592593 0.07407407)
## 24) smoothness_mean< -2.022167 26 1 M (0.96153846 0.03846154)
## 48) smoothness_mean>=-2.340816 24 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean< -2.340816 2 1 M (0.50000000 0.50000000)
## 98) texture_mean>=2.868073 1 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.868073 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.022167 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-2.588521 3 1 B (0.33333333 0.66666667)
## 26) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 2.915767 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.325507 593 246 B (0.41483980 0.58516020)
## 14) compactness_se< -3.476676 462 212 B (0.45887446 0.54112554)
## 28) compactness_se>=-3.883198 167 63 M (0.62275449 0.37724551)
## 56) smoothness_mean>=-2.321775 89 20 M (0.77528090 0.22471910)
## 112) smoothness_worst>=-1.456304 30 1 M (0.96666667 0.03333333) *
## 113) smoothness_worst< -1.456304 59 19 M (0.67796610 0.32203390) *
## 57) smoothness_mean< -2.321775 78 35 B (0.44871795 0.55128205)
## 114) smoothness_worst< -1.598495 23 2 M (0.91304348 0.08695652) *
## 115) smoothness_worst>=-1.598495 55 14 B (0.25454545 0.74545455) *
## 29) compactness_se< -3.883198 295 108 B (0.36610169 0.63389831)
## 58) texture_mean>=2.809391 261 108 B (0.41379310 0.58620690)
## 116) texture_mean< 2.848102 34 9 M (0.73529412 0.26470588) *
## 117) texture_mean>=2.848102 227 83 B (0.36563877 0.63436123) *
## 59) texture_mean< 2.809391 34 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-3.476676 131 34 B (0.25954198 0.74045802)
## 30) smoothness_worst>=-1.502084 56 27 B (0.48214286 0.51785714)
## 60) smoothness_worst< -1.468619 25 4 M (0.84000000 0.16000000)
## 120) texture_mean>=2.8622 18 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.8622 7 3 B (0.42857143 0.57142857) *
## 61) smoothness_worst>=-1.468619 31 6 B (0.19354839 0.80645161)
## 122) smoothness_mean>=-2.049356 3 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.049356 28 3 B (0.10714286 0.89285714) *
## 31) smoothness_worst< -1.502084 75 7 B (0.09333333 0.90666667)
## 62) texture_mean>=3.038537 2 0 M (1.00000000 0.00000000) *
## 63) texture_mean< 3.038537 73 5 B (0.06849315 0.93150685)
## 126) smoothness_worst< -1.568787 19 5 B (0.26315789 0.73684211) *
## 127) smoothness_worst>=-1.568787 54 0 B (0.00000000 1.00000000) *
##
## $trees[[29]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 B (0.47697368 0.52302632)
## 2) texture_worst>=4.362076 703 335 M (0.52347084 0.47652916)
## 4) texture_mean< 3.087399 504 212 M (0.57936508 0.42063492)
## 8) smoothness_mean>=-2.199595 40 5 M (0.87500000 0.12500000)
## 16) texture_mean>=2.918531 29 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.918531 11 5 M (0.54545455 0.45454545)
## 34) texture_mean< 2.899496 6 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=2.899496 5 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.199595 464 207 M (0.55387931 0.44612069)
## 18) smoothness_mean< -2.235862 408 165 M (0.59558824 0.40441176)
## 36) smoothness_mean>=-2.391854 230 71 M (0.69130435 0.30869565)
## 72) smoothness_worst< -1.476997 139 23 M (0.83453237 0.16546763) *
## 73) smoothness_worst>=-1.476997 91 43 B (0.47252747 0.52747253) *
## 37) smoothness_mean< -2.391854 178 84 B (0.47191011 0.52808989)
## 74) smoothness_worst< -1.549836 137 56 M (0.59124088 0.40875912) *
## 75) smoothness_worst>=-1.549836 41 3 B (0.07317073 0.92682927) *
## 19) smoothness_mean>=-2.235862 56 14 B (0.25000000 0.75000000)
## 38) compactness_se< -4.140724 12 2 M (0.83333333 0.16666667)
## 76) smoothness_mean>=-2.21595 10 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.21595 2 0 B (0.00000000 1.00000000) *
## 39) compactness_se>=-4.140724 44 4 B (0.09090909 0.90909091)
## 78) texture_mean>=3.04949 4 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.04949 40 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.087399 199 76 B (0.38190955 0.61809045)
## 10) smoothness_worst>=-1.603555 162 73 B (0.45061728 0.54938272)
## 20) texture_worst< 4.803681 31 6 M (0.80645161 0.19354839)
## 40) smoothness_mean< -2.29363 24 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.29363 7 1 B (0.14285714 0.85714286)
## 82) smoothness_mean>=-2.242961 1 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.242961 6 0 B (0.00000000 1.00000000) *
## 21) texture_worst>=4.803681 131 48 B (0.36641221 0.63358779)
## 42) smoothness_worst< -1.582589 7 0 M (1.00000000 0.00000000) *
## 43) smoothness_worst>=-1.582589 124 41 B (0.33064516 0.66935484)
## 86) texture_mean< 3.171358 31 14 M (0.54838710 0.45161290) *
## 87) texture_mean>=3.171358 93 24 B (0.25806452 0.74193548) *
## 11) smoothness_worst< -1.603555 37 3 B (0.08108108 0.91891892)
## 22) smoothness_mean>=-2.337942 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.337942 35 1 B (0.02857143 0.97142857)
## 46) symmetry_worst< -2.632248 1 0 M (1.00000000 0.00000000) *
## 47) symmetry_worst>=-2.632248 34 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.362076 209 67 B (0.32057416 0.67942584)
## 6) compactness_se>=-4.173143 175 67 B (0.38285714 0.61714286)
## 12) compactness_se< -4.160164 8 0 M (1.00000000 0.00000000) *
## 13) compactness_se>=-4.160164 167 59 B (0.35329341 0.64670659)
## 26) texture_mean< 2.801532 105 49 B (0.46666667 0.53333333)
## 52) texture_worst>=4.178472 27 4 M (0.85185185 0.14814815)
## 104) compactness_se>=-3.892047 23 0 M (1.00000000 0.00000000) *
## 105) compactness_se< -3.892047 4 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.178472 78 26 B (0.33333333 0.66666667)
## 106) compactness_se>=-2.94014 7 1 M (0.85714286 0.14285714) *
## 107) compactness_se< -2.94014 71 20 B (0.28169014 0.71830986) *
## 27) texture_mean>=2.801532 62 10 B (0.16129032 0.83870968)
## 54) texture_worst< 4.138009 4 0 M (1.00000000 0.00000000) *
## 55) texture_worst>=4.138009 58 6 B (0.10344828 0.89655172)
## 110) smoothness_worst>=-1.384694 2 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst< -1.384694 56 4 B (0.07142857 0.92857143) *
## 7) compactness_se< -4.173143 34 0 B (0.00000000 1.00000000) *
##
## $trees[[30]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 B (0.48245614 0.51754386)
## 2) smoothness_mean>=-2.424301 673 308 M (0.54234770 0.45765230)
## 4) symmetry_worst< -2.379234 29 2 M (0.93103448 0.06896552)
## 8) texture_mean>=2.855865 28 1 M (0.96428571 0.03571429)
## 16) smoothness_mean< -2.287736 25 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean>=-2.287736 3 1 M (0.66666667 0.33333333)
## 34) texture_mean>=3.050671 2 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.050671 1 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.855865 1 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-2.379234 644 306 M (0.52484472 0.47515528)
## 10) smoothness_worst>=-1.559144 554 240 M (0.56678700 0.43321300)
## 20) smoothness_worst< -1.536824 76 10 M (0.86842105 0.13157895)
## 40) symmetry_worst< -1.583647 71 6 M (0.91549296 0.08450704)
## 80) texture_mean>=2.6809 69 4 M (0.94202899 0.05797101) *
## 81) texture_mean< 2.6809 2 0 B (0.00000000 1.00000000) *
## 41) symmetry_worst>=-1.583647 5 1 B (0.20000000 0.80000000)
## 82) texture_mean>=3.07122 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.07122 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.536824 478 230 M (0.51882845 0.48117155)
## 42) smoothness_worst>=-1.525694 440 198 M (0.55000000 0.45000000)
## 84) compactness_se>=-4.557422 427 185 M (0.56674473 0.43325527) *
## 85) compactness_se< -4.557422 13 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst< -1.525694 38 6 B (0.15789474 0.84210526)
## 86) smoothness_mean>=-2.170258 6 1 M (0.83333333 0.16666667) *
## 87) smoothness_mean< -2.170258 32 1 B (0.03125000 0.96875000) *
## 11) smoothness_worst< -1.559144 90 24 B (0.26666667 0.73333333)
## 22) compactness_se>=-3.745127 46 23 M (0.50000000 0.50000000)
## 44) texture_worst>=4.585652 22 4 M (0.81818182 0.18181818)
## 88) smoothness_worst>=-1.618016 19 1 M (0.94736842 0.05263158) *
## 89) smoothness_worst< -1.618016 3 0 B (0.00000000 1.00000000) *
## 45) texture_worst< 4.585652 24 5 B (0.20833333 0.79166667)
## 90) texture_mean>=2.969227 3 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.969227 21 2 B (0.09523810 0.90476190) *
## 23) compactness_se< -3.745127 44 1 B (0.02272727 0.97727273)
## 46) smoothness_mean< -2.399947 3 1 B (0.33333333 0.66666667)
## 92) texture_mean< 3.119032 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean>=3.119032 2 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean>=-2.399947 41 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.424301 239 75 B (0.31380753 0.68619247)
## 6) smoothness_worst< -1.551775 182 70 B (0.38461538 0.61538462)
## 12) smoothness_worst>=-1.556752 26 3 M (0.88461538 0.11538462)
## 24) texture_mean>=2.850634 23 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.850634 3 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.556752 156 47 B (0.30128205 0.69871795)
## 26) smoothness_worst< -1.576547 123 47 B (0.38211382 0.61788618)
## 52) symmetry_worst>=-1.781697 42 13 M (0.69047619 0.30952381)
## 104) smoothness_mean< -2.47008 34 6 M (0.82352941 0.17647059) *
## 105) smoothness_mean>=-2.47008 8 1 B (0.12500000 0.87500000) *
## 53) symmetry_worst< -1.781697 81 18 B (0.22222222 0.77777778)
## 106) compactness_se>=-3.514597 23 9 M (0.60869565 0.39130435) *
## 107) compactness_se< -3.514597 58 4 B (0.06896552 0.93103448) *
## 27) smoothness_worst>=-1.576547 33 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.551775 57 5 B (0.08771930 0.91228070)
## 14) symmetry_worst< -1.893206 5 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst>=-1.893206 52 0 B (0.00000000 1.00000000) *
##
## $trees[[31]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 381 B (0.41776316 0.58223684)
## 2) symmetry_worst>=-1.366937 34 8 M (0.76470588 0.23529412)
## 4) compactness_se>=-3.486288 17 0 M (1.00000000 0.00000000) *
## 5) compactness_se< -3.486288 17 8 M (0.52941176 0.47058824)
## 10) compactness_se< -4.00428 7 0 M (1.00000000 0.00000000) *
## 11) compactness_se>=-4.00428 10 2 B (0.20000000 0.80000000)
## 22) smoothness_mean< -2.419235 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.419235 8 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 878 355 B (0.40432802 0.59567198)
## 6) symmetry_worst< -1.56292 731 318 B (0.43502052 0.56497948)
## 12) smoothness_worst>=-1.637109 684 312 B (0.45614035 0.54385965)
## 24) smoothness_mean< -2.299091 435 209 M (0.51954023 0.48045977)
## 48) texture_worst< 4.465917 120 32 M (0.73333333 0.26666667)
## 96) compactness_se< -3.377574 110 22 M (0.80000000 0.20000000) *
## 97) compactness_se>=-3.377574 10 0 B (0.00000000 1.00000000) *
## 49) texture_worst>=4.465917 315 138 B (0.43809524 0.56190476)
## 98) texture_worst>=4.578048 240 120 M (0.50000000 0.50000000) *
## 99) texture_worst< 4.578048 75 18 B (0.24000000 0.76000000) *
## 25) smoothness_mean>=-2.299091 249 86 B (0.34538153 0.65461847)
## 50) compactness_se>=-3.02233 14 1 M (0.92857143 0.07142857)
## 100) texture_mean>=2.81216 13 0 M (1.00000000 0.00000000) *
## 101) texture_mean< 2.81216 1 0 B (0.00000000 1.00000000) *
## 51) compactness_se< -3.02233 235 73 B (0.31063830 0.68936170)
## 102) symmetry_worst>=-1.65118 37 12 M (0.67567568 0.32432432) *
## 103) symmetry_worst< -1.65118 198 48 B (0.24242424 0.75757576) *
## 13) smoothness_worst< -1.637109 47 6 B (0.12765957 0.87234043)
## 26) compactness_se>=-2.979429 7 3 M (0.57142857 0.42857143)
## 52) texture_mean>=3.076827 4 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 3.076827 3 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -2.979429 40 2 B (0.05000000 0.95000000)
## 54) smoothness_mean>=-2.38784 1 0 M (1.00000000 0.00000000) *
## 55) smoothness_mean< -2.38784 39 1 B (0.02564103 0.97435897)
## 110) symmetry_worst>=-1.800994 4 1 B (0.25000000 0.75000000) *
## 111) symmetry_worst< -1.800994 35 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.56292 147 37 B (0.25170068 0.74829932)
## 14) smoothness_mean>=-2.155028 8 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.155028 139 29 B (0.20863309 0.79136691)
## 30) texture_worst>=5.204837 6 0 M (1.00000000 0.00000000) *
## 31) texture_worst< 5.204837 133 23 B (0.17293233 0.82706767)
## 62) smoothness_mean< -2.454281 15 7 B (0.46666667 0.53333333)
## 124) smoothness_mean>=-2.462871 7 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.462871 8 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.454281 118 16 B (0.13559322 0.86440678)
## 126) texture_mean>=3.01402 31 10 B (0.32258065 0.67741935) *
## 127) texture_mean< 3.01402 87 6 B (0.06896552 0.93103448) *
##
## $trees[[32]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 385 B (0.42214912 0.57785088)
## 2) symmetry_worst< -2.49184 28 5 M (0.82142857 0.17857143)
## 4) smoothness_worst< -1.534654 25 2 M (0.92000000 0.08000000)
## 8) texture_mean>=2.861235 23 0 M (1.00000000 0.00000000) *
## 9) texture_mean< 2.861235 2 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.534654 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.49184 884 362 B (0.40950226 0.59049774)
## 6) smoothness_worst>=-1.558926 661 302 B (0.45688351 0.54311649)
## 12) smoothness_mean>=-2.48495 631 302 B (0.47860539 0.52139461)
## 24) smoothness_worst< -1.541278 87 21 M (0.75862069 0.24137931)
## 48) smoothness_mean< -2.313857 81 15 M (0.81481481 0.18518519)
## 96) symmetry_worst< -1.809006 55 1 M (0.98181818 0.01818182) *
## 97) symmetry_worst>=-1.809006 26 12 B (0.46153846 0.53846154) *
## 49) smoothness_mean>=-2.313857 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.541278 544 236 B (0.43382353 0.56617647)
## 50) symmetry_worst>=-2.178473 512 235 B (0.45898438 0.54101562)
## 100) texture_mean>=2.836149 404 196 M (0.51485149 0.48514851) *
## 101) texture_mean< 2.836149 108 27 B (0.25000000 0.75000000) *
## 51) symmetry_worst< -2.178473 32 1 B (0.03125000 0.96875000)
## 102) texture_worst>=4.947241 6 1 B (0.16666667 0.83333333) *
## 103) texture_worst< 4.947241 26 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.48495 30 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.558926 223 60 B (0.26905830 0.73094170)
## 14) smoothness_worst< -1.57166 165 56 B (0.33939394 0.66060606)
## 28) symmetry_worst>=-1.787851 56 25 M (0.55357143 0.44642857)
## 56) texture_mean>=2.933058 47 16 M (0.65957447 0.34042553)
## 112) texture_worst< 4.733599 30 5 M (0.83333333 0.16666667) *
## 113) texture_worst>=4.733599 17 6 B (0.35294118 0.64705882) *
## 57) texture_mean< 2.933058 9 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.787851 109 25 B (0.22935780 0.77064220)
## 58) texture_worst>=4.898911 22 10 M (0.54545455 0.45454545)
## 116) texture_worst< 5.13268 10 0 M (1.00000000 0.00000000) *
## 117) texture_worst>=5.13268 12 2 B (0.16666667 0.83333333) *
## 59) texture_worst< 4.898911 87 13 B (0.14942529 0.85057471)
## 118) smoothness_worst< -1.709736 6 1 M (0.83333333 0.16666667) *
## 119) smoothness_worst>=-1.709736 81 8 B (0.09876543 0.90123457) *
## 15) smoothness_worst>=-1.57166 58 4 B (0.06896552 0.93103448)
## 30) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 31) compactness_se< -2.682598 56 2 B (0.03571429 0.96428571)
## 62) smoothness_mean>=-2.299648 1 0 M (1.00000000 0.00000000) *
## 63) smoothness_mean< -2.299648 55 1 B (0.01818182 0.98181818)
## 126) smoothness_worst< -1.568787 7 1 B (0.14285714 0.85714286) *
## 127) smoothness_worst>=-1.568787 48 0 B (0.00000000 1.00000000) *
##
## $trees[[33]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 382 B (0.41885965 0.58114035)
## 2) symmetry_worst>=-1.353976 49 13 M (0.73469388 0.26530612)
## 4) symmetry_worst< -1.244631 26 2 M (0.92307692 0.07692308)
## 8) compactness_se>=-3.807179 23 0 M (1.00000000 0.00000000) *
## 9) compactness_se< -3.807179 3 1 B (0.33333333 0.66666667)
## 18) texture_mean>=2.933998 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.933998 2 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.244631 23 11 M (0.52173913 0.47826087)
## 10) symmetry_worst>=-1.072749 12 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.072749 11 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.353976 863 346 B (0.40092700 0.59907300)
## 6) smoothness_mean>=-2.425205 656 291 B (0.44359756 0.55640244)
## 12) smoothness_mean< -2.233059 531 260 B (0.48964218 0.51035782)
## 24) texture_worst< 4.555292 249 97 M (0.61044177 0.38955823)
## 48) symmetry_worst< -1.700875 185 55 M (0.70270270 0.29729730)
## 96) compactness_se< -3.48728 138 28 M (0.79710145 0.20289855) *
## 97) compactness_se>=-3.48728 47 20 B (0.42553191 0.57446809) *
## 49) symmetry_worst>=-1.700875 64 22 B (0.34375000 0.65625000)
## 98) texture_worst>=4.517878 9 0 M (1.00000000 0.00000000) *
## 99) texture_worst< 4.517878 55 13 B (0.23636364 0.76363636) *
## 25) texture_worst>=4.555292 282 108 B (0.38297872 0.61702128)
## 50) smoothness_worst>=-1.419909 15 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst< -1.419909 267 93 B (0.34831461 0.65168539)
## 102) smoothness_worst< -1.484675 146 67 B (0.45890411 0.54109589) *
## 103) smoothness_worst>=-1.484675 121 26 B (0.21487603 0.78512397) *
## 13) smoothness_mean>=-2.233059 125 31 B (0.24800000 0.75200000)
## 26) texture_worst>=5.026995 6 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 5.026995 119 25 B (0.21008403 0.78991597)
## 54) smoothness_worst< -1.531558 6 0 M (1.00000000 0.00000000) *
## 55) smoothness_worst>=-1.531558 113 19 B (0.16814159 0.83185841)
## 110) symmetry_worst>=-1.532237 17 8 M (0.52941176 0.47058824) *
## 111) symmetry_worst< -1.532237 96 10 B (0.10416667 0.89583333) *
## 7) smoothness_mean< -2.425205 207 55 B (0.26570048 0.73429952)
## 14) smoothness_worst>=-1.653746 172 55 B (0.31976744 0.68023256)
## 28) smoothness_worst< -1.576547 79 39 B (0.49367089 0.50632911)
## 56) symmetry_worst>=-2.050548 64 25 M (0.60937500 0.39062500)
## 112) compactness_se< -3.427985 56 17 M (0.69642857 0.30357143) *
## 113) compactness_se>=-3.427985 8 0 B (0.00000000 1.00000000) *
## 57) symmetry_worst< -2.050548 15 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst>=-1.576547 93 16 B (0.17204301 0.82795699)
## 58) texture_mean>=3.431166 3 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.431166 90 13 B (0.14444444 0.85555556)
## 118) texture_worst< 4.62656 40 12 B (0.30000000 0.70000000) *
## 119) texture_worst>=4.62656 50 1 B (0.02000000 0.98000000) *
## 15) smoothness_worst< -1.653746 35 0 B (0.00000000 1.00000000) *
##
## $trees[[34]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 415 B (0.45504386 0.54495614)
## 2) smoothness_worst< -1.54469 286 128 M (0.55244755 0.44755245)
## 4) smoothness_worst>=-1.55958 84 16 M (0.80952381 0.19047619)
## 8) compactness_se>=-4.694501 80 12 M (0.85000000 0.15000000)
## 16) texture_mean< 3.353705 78 10 M (0.87179487 0.12820513)
## 32) smoothness_mean< -2.313857 70 6 M (0.91428571 0.08571429)
## 64) symmetry_worst< -1.801798 55 1 M (0.98181818 0.01818182) *
## 65) symmetry_worst>=-1.801798 15 5 M (0.66666667 0.33333333) *
## 33) smoothness_mean>=-2.313857 8 4 M (0.50000000 0.50000000)
## 66) texture_mean< 3.271203 4 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.271203 4 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.353705 2 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.694501 4 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.55958 202 90 B (0.44554455 0.55445545)
## 10) smoothness_worst< -1.568787 170 83 M (0.51176471 0.48823529)
## 20) smoothness_mean>=-2.337942 25 2 M (0.92000000 0.08000000)
## 40) smoothness_mean< -2.294641 18 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.294641 7 2 M (0.71428571 0.28571429)
## 82) smoothness_mean>=-2.214186 5 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.214186 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.337942 145 64 B (0.44137931 0.55862069)
## 42) symmetry_worst>=-1.795801 43 12 M (0.72093023 0.27906977)
## 84) texture_mean>=2.939162 33 5 M (0.84848485 0.15151515) *
## 85) texture_mean< 2.939162 10 3 B (0.30000000 0.70000000) *
## 43) symmetry_worst< -1.795801 102 33 B (0.32352941 0.67647059)
## 86) smoothness_worst< -1.694089 7 0 M (1.00000000 0.00000000) *
## 87) smoothness_worst>=-1.694089 95 26 B (0.27368421 0.72631579) *
## 11) smoothness_worst>=-1.568787 32 3 B (0.09375000 0.90625000)
## 22) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 23) compactness_se< -2.682598 30 1 B (0.03333333 0.96666667)
## 46) smoothness_mean>=-2.296106 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.296106 29 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.54469 626 257 B (0.41054313 0.58945687)
## 6) smoothness_worst>=-1.501069 464 215 B (0.46336207 0.53663793)
## 12) texture_mean< 3.039744 358 173 M (0.51675978 0.48324022)
## 24) texture_mean>=2.967331 101 20 M (0.80198020 0.19801980)
## 48) symmetry_worst>=-1.839419 74 6 M (0.91891892 0.08108108)
## 96) symmetry_worst< -1.471051 68 2 M (0.97058824 0.02941176) *
## 97) symmetry_worst>=-1.471051 6 2 B (0.33333333 0.66666667) *
## 49) symmetry_worst< -1.839419 27 13 B (0.48148148 0.51851852)
## 98) symmetry_worst< -1.878579 13 0 M (1.00000000 0.00000000) *
## 99) symmetry_worst>=-1.878579 14 0 B (0.00000000 1.00000000) *
## 25) texture_mean< 2.967331 257 104 B (0.40466926 0.59533074)
## 50) smoothness_mean< -2.267218 116 46 M (0.60344828 0.39655172)
## 100) texture_worst< 4.543572 76 17 M (0.77631579 0.22368421) *
## 101) texture_worst>=4.543572 40 11 B (0.27500000 0.72500000) *
## 51) smoothness_mean>=-2.267218 141 34 B (0.24113475 0.75886525)
## 102) symmetry_worst>=-1.74232 74 33 B (0.44594595 0.55405405) *
## 103) symmetry_worst< -1.74232 67 1 B (0.01492537 0.98507463) *
## 13) texture_mean>=3.039744 106 30 B (0.28301887 0.71698113)
## 26) compactness_se>=-3.334337 16 2 M (0.87500000 0.12500000)
## 52) smoothness_mean>=-2.420336 14 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.420336 2 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.334337 90 16 B (0.17777778 0.82222222)
## 54) symmetry_worst< -1.822663 17 8 M (0.52941176 0.47058824)
## 108) compactness_se< -3.615775 8 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-3.615775 9 1 B (0.11111111 0.88888889) *
## 55) symmetry_worst>=-1.822663 73 7 B (0.09589041 0.90410959)
## 110) smoothness_worst< -1.483884 4 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.483884 69 3 B (0.04347826 0.95652174) *
## 7) smoothness_worst< -1.501069 162 42 B (0.25925926 0.74074074)
## 14) texture_worst>=4.536474 107 39 B (0.36448598 0.63551402)
## 28) smoothness_worst< -1.510008 79 38 B (0.48101266 0.51898734)
## 56) texture_worst< 4.774294 41 13 M (0.68292683 0.31707317)
## 112) smoothness_mean>=-2.405234 34 6 M (0.82352941 0.17647059) *
## 113) smoothness_mean< -2.405234 7 0 B (0.00000000 1.00000000) *
## 57) texture_worst>=4.774294 38 10 B (0.26315789 0.73684211)
## 114) smoothness_worst>=-1.52112 7 0 M (1.00000000 0.00000000) *
## 115) smoothness_worst< -1.52112 31 3 B (0.09677419 0.90322581) *
## 29) smoothness_worst>=-1.510008 28 1 B (0.03571429 0.96428571)
## 58) compactness_se>=-3.44344 3 1 B (0.33333333 0.66666667)
## 116) texture_mean< 3.145585 1 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.145585 2 0 B (0.00000000 1.00000000) *
## 59) compactness_se< -3.44344 25 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.536474 55 3 B (0.05454545 0.94545455)
## 30) smoothness_mean>=-2.194169 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=2.816952 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.816952 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.194169 52 1 B (0.01923077 0.98076923)
## 62) smoothness_worst< -1.541278 2 1 M (0.50000000 0.50000000)
## 124) texture_mean>=2.773152 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.773152 1 0 B (0.00000000 1.00000000) *
## 63) smoothness_worst>=-1.541278 50 0 B (0.00000000 1.00000000) *
##
## $trees[[35]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 408 B (0.44736842 0.55263158)
## 2) compactness_se>=-3.721197 402 177 M (0.55970149 0.44029851)
## 4) compactness_se< -3.575734 76 10 M (0.86842105 0.13157895)
## 8) texture_mean>=2.647471 73 7 M (0.90410959 0.09589041)
## 16) texture_mean< 3.07431 54 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.07431 19 7 M (0.63157895 0.36842105)
## 34) symmetry_worst>=-1.999513 14 2 M (0.85714286 0.14285714)
## 68) texture_mean>=3.087384 12 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 3.087384 2 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst< -1.999513 5 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.647471 3 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-3.575734 326 159 B (0.48773006 0.51226994)
## 10) smoothness_worst>=-1.618016 298 141 M (0.52684564 0.47315436)
## 20) texture_worst>=5.016194 18 0 M (1.00000000 0.00000000) *
## 21) texture_worst< 5.016194 280 139 B (0.49642857 0.50357143)
## 42) smoothness_worst< -1.595509 16 1 M (0.93750000 0.06250000)
## 84) compactness_se< -3.215213 14 0 M (1.00000000 0.00000000) *
## 85) compactness_se>=-3.215213 2 1 M (0.50000000 0.50000000) *
## 43) smoothness_worst>=-1.595509 264 124 B (0.46969697 0.53030303)
## 86) smoothness_mean>=-2.294122 142 60 M (0.57746479 0.42253521) *
## 87) smoothness_mean< -2.294122 122 42 B (0.34426230 0.65573770) *
## 11) smoothness_worst< -1.618016 28 2 B (0.07142857 0.92857143)
## 22) smoothness_worst< -1.707409 3 1 M (0.66666667 0.33333333)
## 44) texture_mean< 3.103494 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst>=-1.707409 25 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 510 183 B (0.35882353 0.64117647)
## 6) texture_worst>=4.507583 346 148 B (0.42774566 0.57225434)
## 12) smoothness_mean>=-2.407891 244 119 M (0.51229508 0.48770492)
## 24) smoothness_mean< -2.382983 34 4 M (0.88235294 0.11764706)
## 48) texture_mean>=2.920077 32 2 M (0.93750000 0.06250000)
## 96) symmetry_worst>=-2.212871 30 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.920077 2 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.382983 210 95 B (0.45238095 0.54761905)
## 50) texture_mean< 2.903338 56 15 M (0.73214286 0.26785714)
## 100) texture_worst< 4.637071 43 5 M (0.88372093 0.11627907) *
## 101) texture_worst>=4.637071 13 3 B (0.23076923 0.76923077) *
## 51) texture_mean>=2.903338 154 54 B (0.35064935 0.64935065)
## 102) smoothness_worst>=-1.472892 70 30 M (0.57142857 0.42857143) *
## 103) smoothness_worst< -1.472892 84 14 B (0.16666667 0.83333333) *
## 13) smoothness_mean< -2.407891 102 23 B (0.22549020 0.77450980)
## 26) texture_worst>=4.853342 44 17 B (0.38636364 0.61363636)
## 52) texture_worst< 4.985267 19 6 M (0.68421053 0.31578947)
## 104) texture_mean< 3.162414 13 0 M (1.00000000 0.00000000) *
## 105) texture_mean>=3.162414 6 0 B (0.00000000 1.00000000) *
## 53) texture_worst>=4.985267 25 4 B (0.16000000 0.84000000)
## 106) smoothness_mean>=-2.427246 3 1 M (0.66666667 0.33333333) *
## 107) smoothness_mean< -2.427246 22 2 B (0.09090909 0.90909091) *
## 27) texture_worst< 4.853342 58 6 B (0.10344828 0.89655172)
## 54) symmetry_worst>=-1.541105 17 6 B (0.35294118 0.64705882)
## 108) smoothness_mean< -2.449189 4 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean>=-2.449189 13 2 B (0.15384615 0.84615385) *
## 55) symmetry_worst< -1.541105 41 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.507583 164 35 B (0.21341463 0.78658537)
## 14) smoothness_mean< -2.411844 42 18 B (0.42857143 0.57142857)
## 28) symmetry_worst>=-1.963801 28 10 M (0.64285714 0.35714286)
## 56) texture_worst< 4.465917 22 4 M (0.81818182 0.18181818)
## 112) compactness_se>=-4.531581 19 1 M (0.94736842 0.05263158) *
## 113) compactness_se< -4.531581 3 0 B (0.00000000 1.00000000) *
## 57) texture_worst>=4.465917 6 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.963801 14 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.411844 122 17 B (0.13934426 0.86065574)
## 30) symmetry_worst< -2.391709 5 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst>=-2.391709 117 12 B (0.10256410 0.89743590)
## 62) compactness_se>=-3.892047 22 10 B (0.45454545 0.54545455)
## 124) smoothness_worst>=-1.482701 11 1 M (0.90909091 0.09090909) *
## 125) smoothness_worst< -1.482701 11 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.892047 95 2 B (0.02105263 0.97894737)
## 126) smoothness_worst>=-1.42613 3 1 M (0.66666667 0.33333333) *
## 127) smoothness_worst< -1.42613 92 0 B (0.00000000 1.00000000) *
##
## $trees[[36]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 398 B (0.43640351 0.56359649)
## 2) symmetry_worst>=-1.424186 75 22 M (0.70666667 0.29333333)
## 4) texture_mean>=2.77286 58 11 M (0.81034483 0.18965517)
## 8) smoothness_worst>=-1.49649 46 3 M (0.93478261 0.06521739)
## 16) compactness_se< -2.524297 44 1 M (0.97727273 0.02272727)
## 32) compactness_se>=-4.171724 40 0 M (1.00000000 0.00000000) *
## 33) compactness_se< -4.171724 4 1 M (0.75000000 0.25000000)
## 66) texture_mean>=3.068796 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.068796 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.524297 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.49649 12 4 B (0.33333333 0.66666667)
## 18) texture_mean>=3.126045 4 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.126045 8 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.77286 17 6 B (0.35294118 0.64705882)
## 10) symmetry_worst>=-1.195967 6 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.195967 11 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.424186 837 345 B (0.41218638 0.58781362)
## 6) smoothness_mean< -2.23446 698 311 B (0.44555874 0.55444126)
## 12) compactness_se>=-3.466377 133 50 M (0.62406015 0.37593985)
## 24) compactness_se< -3.391153 58 7 M (0.87931034 0.12068966)
## 48) smoothness_mean< -2.262968 55 4 M (0.92727273 0.07272727)
## 96) smoothness_mean>=-2.562637 54 3 M (0.94444444 0.05555556) *
## 97) smoothness_mean< -2.562637 1 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.262968 3 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.391153 75 32 B (0.42666667 0.57333333)
## 50) texture_mean>=3.038537 32 5 M (0.84375000 0.15625000)
## 100) smoothness_worst>=-1.647098 28 2 M (0.92857143 0.07142857) *
## 101) smoothness_worst< -1.647098 4 1 B (0.25000000 0.75000000) *
## 51) texture_mean< 3.038537 43 5 B (0.11627907 0.88372093)
## 102) smoothness_mean>=-2.242902 4 0 M (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.242902 39 1 B (0.02564103 0.97435897) *
## 13) compactness_se< -3.466377 565 228 B (0.40353982 0.59646018)
## 26) smoothness_mean>=-2.251418 27 5 M (0.81481481 0.18518519)
## 52) smoothness_worst>=-1.46195 21 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.46195 6 1 B (0.16666667 0.83333333)
## 106) texture_mean>=3.037597 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean< 3.037597 5 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.251418 538 206 B (0.38289963 0.61710037)
## 54) smoothness_worst>=-1.424105 15 1 M (0.93333333 0.06666667)
## 108) smoothness_mean>=-2.397334 14 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.397334 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst< -1.424105 523 192 B (0.36711281 0.63288719)
## 110) smoothness_mean< -2.299091 430 177 B (0.41162791 0.58837209) *
## 111) smoothness_mean>=-2.299091 93 15 B (0.16129032 0.83870968) *
## 7) smoothness_mean>=-2.23446 139 34 B (0.24460432 0.75539568)
## 14) texture_mean>=3.209345 4 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.209345 135 30 B (0.22222222 0.77777778)
## 30) symmetry_worst>=-1.765259 62 23 B (0.37096774 0.62903226)
## 60) smoothness_worst>=-1.468303 33 12 M (0.63636364 0.36363636)
## 120) smoothness_worst< -1.423212 14 0 M (1.00000000 0.00000000) *
## 121) smoothness_worst>=-1.423212 19 7 B (0.36842105 0.63157895) *
## 61) smoothness_worst< -1.468303 29 2 B (0.06896552 0.93103448)
## 122) texture_mean>=3.103097 2 0 M (1.00000000 0.00000000) *
## 123) texture_mean< 3.103097 27 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.765259 73 7 B (0.09589041 0.90410959)
## 62) symmetry_worst< -2.354921 4 0 M (1.00000000 0.00000000) *
## 63) symmetry_worst>=-2.354921 69 3 B (0.04347826 0.95652174)
## 126) compactness_se>=-3.02233 1 0 M (1.00000000 0.00000000) *
## 127) compactness_se< -3.02233 68 2 B (0.02941176 0.97058824) *
##
## $trees[[37]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 M (0.53399123 0.46600877)
## 2) texture_worst>=4.50835 620 244 M (0.60645161 0.39354839)
## 4) smoothness_worst< -1.429075 556 204 M (0.63309353 0.36690647)
## 8) smoothness_mean>=-2.566967 546 194 M (0.64468864 0.35531136)
## 16) symmetry_worst< -1.750953 273 76 M (0.72161172 0.27838828)
## 32) symmetry_worst>=-1.862978 66 2 M (0.96969697 0.03030303)
## 64) compactness_se< -3.586422 52 0 M (1.00000000 0.00000000) *
## 65) compactness_se>=-3.586422 14 2 M (0.85714286 0.14285714) *
## 33) symmetry_worst< -1.862978 207 74 M (0.64251208 0.35748792)
## 66) symmetry_worst< -1.931815 173 48 M (0.72254335 0.27745665) *
## 67) symmetry_worst>=-1.931815 34 8 B (0.23529412 0.76470588) *
## 17) symmetry_worst>=-1.750953 273 118 M (0.56776557 0.43223443)
## 34) symmetry_worst>=-1.724518 249 97 M (0.61044177 0.38955823)
## 68) compactness_se< -3.859436 135 35 M (0.74074074 0.25925926) *
## 69) compactness_se>=-3.859436 114 52 B (0.45614035 0.54385965) *
## 35) symmetry_worst< -1.724518 24 3 B (0.12500000 0.87500000)
## 70) texture_worst>=5.020647 3 0 M (1.00000000 0.00000000) *
## 71) texture_worst< 5.020647 21 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.566967 10 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.429075 64 24 B (0.37500000 0.62500000)
## 10) symmetry_worst>=-1.529476 16 1 M (0.93750000 0.06250000)
## 20) smoothness_mean>=-2.347148 15 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean< -2.347148 1 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.529476 48 9 B (0.18750000 0.81250000)
## 22) texture_worst< 4.624204 7 1 M (0.85714286 0.14285714)
## 44) texture_mean>=2.924481 6 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.924481 1 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.624204 41 3 B (0.07317073 0.92682927)
## 46) texture_mean>=3.26885 1 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.26885 40 2 B (0.05000000 0.95000000)
## 94) texture_mean< 2.995481 12 2 B (0.16666667 0.83333333) *
## 95) texture_mean>=2.995481 28 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.50835 292 111 B (0.38013699 0.61986301)
## 6) smoothness_mean< -2.267218 182 85 B (0.46703297 0.53296703)
## 12) smoothness_worst>=-1.496838 49 14 M (0.71428571 0.28571429)
## 24) compactness_se>=-4.122059 40 5 M (0.87500000 0.12500000)
## 48) texture_worst< 4.487999 38 3 M (0.92105263 0.07894737)
## 96) symmetry_worst< -1.440359 35 1 M (0.97142857 0.02857143) *
## 97) symmetry_worst>=-1.440359 3 1 B (0.33333333 0.66666667) *
## 49) texture_worst>=4.487999 2 0 B (0.00000000 1.00000000) *
## 25) compactness_se< -4.122059 9 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.496838 133 50 B (0.37593985 0.62406015)
## 26) symmetry_worst< -1.698675 92 45 B (0.48913043 0.51086957)
## 52) compactness_se>=-3.714667 34 7 M (0.79411765 0.20588235)
## 104) smoothness_mean>=-2.456711 26 2 M (0.92307692 0.07692308) *
## 105) smoothness_mean< -2.456711 8 3 B (0.37500000 0.62500000) *
## 53) compactness_se< -3.714667 58 18 B (0.31034483 0.68965517)
## 106) symmetry_worst>=-1.756915 5 0 M (1.00000000 0.00000000) *
## 107) symmetry_worst< -1.756915 53 13 B (0.24528302 0.75471698) *
## 27) symmetry_worst>=-1.698675 41 5 B (0.12195122 0.87804878)
## 54) compactness_se< -4.281648 10 5 M (0.50000000 0.50000000)
## 108) texture_mean>=2.975525 5 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.975525 5 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-4.281648 31 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.267218 110 26 B (0.23636364 0.76363636)
## 14) smoothness_mean>=-2.172878 34 15 M (0.55882353 0.44117647)
## 28) smoothness_worst< -1.473124 11 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.473124 23 8 B (0.34782609 0.65217391)
## 58) symmetry_worst>=-1.596878 10 2 M (0.80000000 0.20000000)
## 116) smoothness_mean>=-2.087477 8 0 M (1.00000000 0.00000000) *
## 117) smoothness_mean< -2.087477 2 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.596878 13 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.172878 76 7 B (0.09210526 0.90789474)
## 30) compactness_se>=-3.084108 6 1 M (0.83333333 0.16666667)
## 60) texture_mean< 2.81718 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.81718 1 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.084108 70 2 B (0.02857143 0.97142857)
## 62) smoothness_worst>=-1.429447 2 0 M (1.00000000 0.00000000) *
## 63) smoothness_worst< -1.429447 68 0 B (0.00000000 1.00000000) *
##
## $trees[[38]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 449 B (0.49232456 0.50767544)
## 2) symmetry_worst>=-1.815934 530 230 M (0.56603774 0.43396226)
## 4) symmetry_worst< -1.809351 26 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.809351 504 230 M (0.54365079 0.45634921)
## 10) compactness_se>=-4.547852 473 206 M (0.56448203 0.43551797)
## 20) texture_mean>=2.929857 317 119 M (0.62460568 0.37539432)
## 40) texture_worst< 4.786372 163 41 M (0.74846626 0.25153374)
## 80) compactness_se>=-4.291103 157 35 M (0.77707006 0.22292994) *
## 81) compactness_se< -4.291103 6 0 B (0.00000000 1.00000000) *
## 41) texture_worst>=4.786372 154 76 B (0.49350649 0.50649351)
## 82) texture_worst>=4.818867 130 55 M (0.57692308 0.42307692) *
## 83) texture_worst< 4.818867 24 1 B (0.04166667 0.95833333) *
## 21) texture_mean< 2.929857 156 69 B (0.44230769 0.55769231)
## 42) texture_mean< 2.899771 123 55 M (0.55284553 0.44715447)
## 84) compactness_se>=-3.982052 93 31 M (0.66666667 0.33333333) *
## 85) compactness_se< -3.982052 30 6 B (0.20000000 0.80000000) *
## 43) texture_mean>=2.899771 33 1 B (0.03030303 0.96969697)
## 86) symmetry_worst>=-1.383772 1 0 M (1.00000000 0.00000000) *
## 87) symmetry_worst< -1.383772 32 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.547852 31 7 B (0.22580645 0.77419355)
## 22) texture_worst< 4.622562 11 4 M (0.63636364 0.36363636)
## 44) texture_mean>=2.912851 7 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.912851 4 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.622562 20 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.815934 382 149 B (0.39005236 0.60994764)
## 6) texture_worst>=4.897936 87 31 M (0.64367816 0.35632184)
## 12) symmetry_worst>=-2.207988 69 14 M (0.79710145 0.20289855)
## 24) texture_mean< 3.361554 63 8 M (0.87301587 0.12698413)
## 48) compactness_se>=-4.758524 59 4 M (0.93220339 0.06779661)
## 96) smoothness_worst>=-1.62752 57 2 M (0.96491228 0.03508772) *
## 97) smoothness_worst< -1.62752 2 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.758524 4 0 B (0.00000000 1.00000000) *
## 25) texture_mean>=3.361554 6 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -2.207988 18 1 B (0.05555556 0.94444444)
## 26) smoothness_mean>=-2.282229 1 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean< -2.282229 17 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.897936 295 93 B (0.31525424 0.68474576)
## 14) smoothness_worst< -1.474648 228 90 B (0.39473684 0.60526316)
## 28) smoothness_worst>=-1.482898 24 3 M (0.87500000 0.12500000)
## 56) compactness_se>=-3.967101 22 1 M (0.95454545 0.04545455)
## 112) texture_mean>=2.721909 21 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.721909 1 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -3.967101 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.482898 204 69 B (0.33823529 0.66176471)
## 58) texture_worst>=4.575448 70 34 M (0.51428571 0.48571429)
## 116) smoothness_worst>=-1.580846 51 15 M (0.70588235 0.29411765) *
## 117) smoothness_worst< -1.580846 19 0 B (0.00000000 1.00000000) *
## 59) texture_worst< 4.575448 134 33 B (0.24626866 0.75373134)
## 118) smoothness_worst< -1.595961 47 22 M (0.53191489 0.46808511) *
## 119) smoothness_worst>=-1.595961 87 8 B (0.09195402 0.90804598) *
## 15) smoothness_worst>=-1.474648 67 3 B (0.04477612 0.95522388)
## 30) smoothness_mean< -2.352223 8 3 B (0.37500000 0.62500000)
## 60) texture_mean>=2.830895 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.830895 5 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.352223 59 0 B (0.00000000 1.00000000) *
##
## $trees[[39]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 380 B (0.41666667 0.58333333)
## 2) smoothness_mean>=-2.423454 691 318 B (0.46020260 0.53979740)
## 4) texture_worst>=4.389172 505 243 M (0.51881188 0.48118812)
## 8) texture_mean< 3.35917 484 222 M (0.54132231 0.45867769)
## 16) smoothness_mean< -2.093138 465 203 M (0.56344086 0.43655914)
## 32) symmetry_worst>=-2.052205 355 134 M (0.62253521 0.37746479)
## 64) texture_mean>=3.054236 107 22 M (0.79439252 0.20560748) *
## 65) texture_mean< 3.054236 248 112 M (0.54838710 0.45161290) *
## 33) symmetry_worst< -2.052205 110 41 B (0.37272727 0.62727273)
## 66) symmetry_worst< -2.115205 77 36 M (0.53246753 0.46753247) *
## 67) symmetry_worst>=-2.115205 33 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.093138 19 0 B (0.00000000 1.00000000) *
## 9) texture_mean>=3.35917 21 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.389172 186 56 B (0.30107527 0.69892473)
## 10) texture_worst< 4.313991 135 50 B (0.37037037 0.62962963)
## 20) symmetry_worst>=-1.828847 77 38 M (0.50649351 0.49350649)
## 40) compactness_se>=-3.647113 36 9 M (0.75000000 0.25000000)
## 80) texture_mean< 2.801532 25 2 M (0.92000000 0.08000000) *
## 81) texture_mean>=2.801532 11 4 B (0.36363636 0.63636364) *
## 41) compactness_se< -3.647113 41 12 B (0.29268293 0.70731707)
## 82) smoothness_mean< -2.411844 9 1 M (0.88888889 0.11111111) *
## 83) smoothness_mean>=-2.411844 32 4 B (0.12500000 0.87500000) *
## 21) symmetry_worst< -1.828847 58 11 B (0.18965517 0.81034483)
## 42) compactness_se< -3.57366 23 11 B (0.47826087 0.52173913)
## 84) smoothness_worst>=-1.499666 10 1 M (0.90000000 0.10000000) *
## 85) smoothness_worst< -1.499666 13 2 B (0.15384615 0.84615385) *
## 43) compactness_se>=-3.57366 35 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.313991 51 6 B (0.11764706 0.88235294)
## 22) smoothness_worst>=-1.428351 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_worst< -1.428351 49 4 B (0.08163265 0.91836735)
## 46) compactness_se>=-3.095053 8 3 B (0.37500000 0.62500000)
## 92) texture_mean>=2.911683 3 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.911683 5 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.095053 41 1 B (0.02439024 0.97560976)
## 94) symmetry_worst< -1.973981 8 1 B (0.12500000 0.87500000) *
## 95) symmetry_worst>=-1.973981 33 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.423454 221 62 B (0.28054299 0.71945701)
## 6) symmetry_worst>=-1.496954 15 3 M (0.80000000 0.20000000)
## 12) texture_mean>=2.977947 12 0 M (1.00000000 0.00000000) *
## 13) texture_mean< 2.977947 3 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.496954 206 50 B (0.24271845 0.75728155)
## 14) texture_mean>=3.388429 7 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.388429 199 43 B (0.21608040 0.78391960)
## 30) compactness_se< -4.687525 23 9 M (0.60869565 0.39130435)
## 60) compactness_se>=-4.706178 14 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.687525 176 29 B (0.16477273 0.83522727)
## 62) texture_worst< 3.96146 12 3 M (0.75000000 0.25000000)
## 124) texture_mean>=2.754513 9 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.754513 3 0 B (0.00000000 1.00000000) *
## 63) texture_worst>=3.96146 164 20 B (0.12195122 0.87804878)
## 126) smoothness_worst< -1.720903 4 1 M (0.75000000 0.25000000) *
## 127) smoothness_worst>=-1.720903 160 17 B (0.10625000 0.89375000) *
##
## $trees[[40]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 348 B (0.38157895 0.61842105)
## 2) symmetry_worst>=-1.366937 40 10 M (0.75000000 0.25000000)
## 4) symmetry_worst< -1.23578 22 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.23578 18 8 B (0.44444444 0.55555556)
## 10) smoothness_worst>=-1.451731 7 0 M (1.00000000 0.00000000) *
## 11) smoothness_worst< -1.451731 11 1 B (0.09090909 0.90909091)
## 22) texture_mean>=3.158816 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.158816 10 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 872 318 B (0.36467890 0.63532110)
## 6) smoothness_worst< -1.439482 757 296 B (0.39101717 0.60898283)
## 12) symmetry_worst>=-2.232873 679 283 B (0.41678940 0.58321060)
## 24) symmetry_worst< -1.634569 467 218 B (0.46680942 0.53319058)
## 48) smoothness_worst>=-1.604472 410 200 M (0.51219512 0.48780488)
## 96) smoothness_mean< -2.349846 218 86 M (0.60550459 0.39449541) *
## 97) smoothness_mean>=-2.349846 192 78 B (0.40625000 0.59375000) *
## 49) smoothness_worst< -1.604472 57 8 B (0.14035088 0.85964912)
## 98) texture_mean< 2.966301 13 5 B (0.38461538 0.61538462) *
## 99) texture_mean>=2.966301 44 3 B (0.06818182 0.93181818) *
## 25) symmetry_worst>=-1.634569 212 65 B (0.30660377 0.69339623)
## 50) symmetry_worst>=-1.549706 109 48 B (0.44036697 0.55963303)
## 100) smoothness_worst< -1.513087 42 13 M (0.69047619 0.30952381) *
## 101) smoothness_worst>=-1.513087 67 19 B (0.28358209 0.71641791) *
## 51) symmetry_worst< -1.549706 103 17 B (0.16504854 0.83495146)
## 102) smoothness_worst>=-1.470752 10 2 M (0.80000000 0.20000000) *
## 103) smoothness_worst< -1.470752 93 9 B (0.09677419 0.90322581) *
## 13) symmetry_worst< -2.232873 78 13 B (0.16666667 0.83333333)
## 26) smoothness_mean>=-2.307549 28 13 B (0.46428571 0.53571429)
## 52) smoothness_worst< -1.59459 11 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst>=-1.59459 17 2 B (0.11764706 0.88235294)
## 106) texture_worst>=4.618547 2 0 M (1.00000000 0.00000000) *
## 107) texture_worst< 4.618547 15 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.307549 50 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.439482 115 22 B (0.19130435 0.80869565)
## 14) smoothness_mean>=-2.079457 7 1 M (0.85714286 0.14285714)
## 28) smoothness_mean< -1.889548 6 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-1.889548 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.079457 108 16 B (0.14814815 0.85185185)
## 30) texture_mean>=3.242184 4 0 M (1.00000000 0.00000000) *
## 31) texture_mean< 3.242184 104 12 B (0.11538462 0.88461538)
## 62) compactness_se< -4.38342 8 3 M (0.62500000 0.37500000)
## 124) texture_mean>=2.904788 5 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.904788 3 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-4.38342 96 7 B (0.07291667 0.92708333)
## 126) smoothness_worst>=-1.334845 1 0 M (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.334845 95 6 B (0.06315789 0.93684211) *
##
## $trees[[41]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 368 B (0.40350877 0.59649123)
## 2) smoothness_worst< -1.434076 810 344 B (0.42469136 0.57530864)
## 4) smoothness_worst>=-1.482699 221 102 M (0.53846154 0.46153846)
## 8) symmetry_worst>=-1.721298 118 33 M (0.72033898 0.27966102)
## 16) symmetry_worst< -1.244631 110 25 M (0.77272727 0.22727273)
## 32) compactness_se>=-4.512898 104 19 M (0.81730769 0.18269231)
## 64) compactness_se>=-3.465064 28 0 M (1.00000000 0.00000000) *
## 65) compactness_se< -3.465064 76 19 M (0.75000000 0.25000000) *
## 33) compactness_se< -4.512898 6 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.244631 8 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.721298 103 34 B (0.33009709 0.66990291)
## 18) smoothness_worst< -1.480334 14 0 M (1.00000000 0.00000000) *
## 19) smoothness_worst>=-1.480334 89 20 B (0.22471910 0.77528090)
## 38) texture_worst>=4.877645 19 4 M (0.78947368 0.21052632)
## 76) compactness_se>=-4.054503 15 0 M (1.00000000 0.00000000) *
## 77) compactness_se< -4.054503 4 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 4.877645 70 5 B (0.07142857 0.92857143)
## 78) smoothness_worst< -1.477193 5 2 B (0.40000000 0.60000000) *
## 79) smoothness_worst>=-1.477193 65 3 B (0.04615385 0.95384615) *
## 5) smoothness_worst< -1.482699 589 225 B (0.38200340 0.61799660)
## 10) compactness_se< -4.49319 77 31 M (0.59740260 0.40259740)
## 20) compactness_se>=-4.779408 65 19 M (0.70769231 0.29230769)
## 40) texture_worst>=4.712681 30 1 M (0.96666667 0.03333333)
## 80) texture_mean< 3.262924 29 0 M (1.00000000 0.00000000) *
## 81) texture_mean>=3.262924 1 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.712681 35 17 B (0.48571429 0.51428571)
## 82) smoothness_mean>=-2.422683 17 3 M (0.82352941 0.17647059) *
## 83) smoothness_mean< -2.422683 18 3 B (0.16666667 0.83333333) *
## 21) compactness_se< -4.779408 12 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.49319 512 179 B (0.34960938 0.65039062)
## 22) compactness_se>=-3.721197 227 102 B (0.44933921 0.55066079)
## 44) compactness_se< -3.657776 18 1 M (0.94444444 0.05555556)
## 88) texture_worst< 4.828083 15 0 M (1.00000000 0.00000000) *
## 89) texture_worst>=4.828083 3 1 M (0.66666667 0.33333333) *
## 45) compactness_se>=-3.657776 209 85 B (0.40669856 0.59330144)
## 90) texture_mean>=3.035431 87 36 M (0.58620690 0.41379310) *
## 91) texture_mean< 3.035431 122 34 B (0.27868852 0.72131148) *
## 23) compactness_se< -3.721197 285 77 B (0.27017544 0.72982456)
## 46) compactness_se< -3.869459 226 77 B (0.34070796 0.65929204)
## 92) texture_worst>=5.001873 32 8 M (0.75000000 0.25000000) *
## 93) texture_worst< 5.001873 194 53 B (0.27319588 0.72680412) *
## 47) compactness_se>=-3.869459 59 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.434076 102 24 B (0.23529412 0.76470588)
## 6) symmetry_worst>=-1.270655 5 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.270655 97 19 B (0.19587629 0.80412371)
## 14) compactness_se>=-2.695649 2 0 M (1.00000000 0.00000000) *
## 15) compactness_se< -2.695649 95 17 B (0.17894737 0.82105263)
## 30) compactness_se< -4.186419 5 2 M (0.60000000 0.40000000)
## 60) texture_mean>=2.950291 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.950291 2 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.186419 90 14 B (0.15555556 0.84444444)
## 62) compactness_se>=-3.998097 59 14 B (0.23728814 0.76271186)
## 124) compactness_se< -3.768789 7 1 M (0.85714286 0.14285714) *
## 125) compactness_se>=-3.768789 52 8 B (0.15384615 0.84615385) *
## 63) compactness_se< -3.998097 31 0 B (0.00000000 1.00000000) *
##
## $trees[[42]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 409 B (0.44846491 0.55153509)
## 2) texture_worst< 4.067485 63 18 M (0.71428571 0.28571429)
## 4) texture_mean>=2.650752 45 6 M (0.86666667 0.13333333)
## 8) compactness_se< -3.48221 39 2 M (0.94871795 0.05128205)
## 16) smoothness_mean>=-2.466148 38 1 M (0.97368421 0.02631579)
## 32) compactness_se>=-4.044115 37 0 M (1.00000000 0.00000000) *
## 33) compactness_se< -4.044115 1 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.466148 1 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-3.48221 6 2 B (0.33333333 0.66666667)
## 18) texture_mean< 2.690691 2 0 M (1.00000000 0.00000000) *
## 19) texture_mean>=2.690691 4 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.650752 18 6 B (0.33333333 0.66666667)
## 10) texture_mean< 2.525679 6 0 M (1.00000000 0.00000000) *
## 11) texture_mean>=2.525679 12 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.067485 849 364 B (0.42873969 0.57126031)
## 6) symmetry_worst>=-2.01934 707 327 B (0.46251768 0.53748232)
## 12) symmetry_worst< -1.990832 26 1 M (0.96153846 0.03846154)
## 24) smoothness_mean< -2.261392 22 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.261392 4 1 M (0.75000000 0.25000000)
## 50) texture_mean>=2.960755 3 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.960755 1 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.990832 681 302 B (0.44346549 0.55653451)
## 26) texture_mean>=2.876103 534 261 B (0.48876404 0.51123596)
## 52) texture_mean< 2.893423 33 2 M (0.93939394 0.06060606)
## 104) symmetry_worst< -1.684827 30 0 M (1.00000000 0.00000000) *
## 105) symmetry_worst>=-1.684827 3 1 B (0.33333333 0.66666667) *
## 53) texture_mean>=2.893423 501 230 B (0.45908184 0.54091816)
## 106) smoothness_mean< -2.114071 473 229 B (0.48414376 0.51585624) *
## 107) smoothness_mean>=-2.114071 28 1 B (0.03571429 0.96428571) *
## 27) texture_mean< 2.876103 147 41 B (0.27891156 0.72108844)
## 54) smoothness_mean>=-2.15202 11 1 M (0.90909091 0.09090909)
## 108) compactness_se>=-3.643968 10 0 M (1.00000000 0.00000000) *
## 109) compactness_se< -3.643968 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_mean< -2.15202 136 31 B (0.22794118 0.77205882)
## 110) texture_mean< 2.841997 86 29 B (0.33720930 0.66279070) *
## 111) texture_mean>=2.841997 50 2 B (0.04000000 0.96000000) *
## 7) symmetry_worst< -2.01934 142 37 B (0.26056338 0.73943662)
## 14) smoothness_mean>=-2.394379 83 30 B (0.36144578 0.63855422)
## 28) smoothness_mean< -2.382983 13 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.382983 70 17 B (0.24285714 0.75714286)
## 58) compactness_se< -4.492707 5 0 M (1.00000000 0.00000000) *
## 59) compactness_se>=-4.492707 65 12 B (0.18461538 0.81538462)
## 118) smoothness_worst>=-1.518057 31 12 B (0.38709677 0.61290323) *
## 119) smoothness_worst< -1.518057 34 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.394379 59 7 B (0.11864407 0.88135593)
## 30) compactness_se>=-3.530926 10 3 M (0.70000000 0.30000000)
## 60) compactness_se< -3.188171 6 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-3.188171 4 1 B (0.25000000 0.75000000)
## 122) texture_worst< 4.59095 1 0 M (1.00000000 0.00000000) *
## 123) texture_worst>=4.59095 3 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.530926 49 0 B (0.00000000 1.00000000) *
##
## $trees[[43]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 424 M (0.53508772 0.46491228)
## 2) symmetry_worst>=-2.052205 766 326 M (0.57441253 0.42558747)
## 4) smoothness_worst< -1.363268 747 310 M (0.58500669 0.41499331)
## 8) compactness_se>=-3.721197 323 108 M (0.66563467 0.33436533)
## 16) smoothness_mean>=-2.325189 173 39 M (0.77456647 0.22543353)
## 32) compactness_se< -3.494301 68 4 M (0.94117647 0.05882353)
## 64) texture_mean>=2.649195 65 1 M (0.98461538 0.01538462) *
## 65) texture_mean< 2.649195 3 0 B (0.00000000 1.00000000) *
## 33) compactness_se>=-3.494301 105 35 M (0.66666667 0.33333333)
## 66) compactness_se>=-3.447524 93 23 M (0.75268817 0.24731183) *
## 67) compactness_se< -3.447524 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.325189 150 69 M (0.54000000 0.46000000)
## 34) smoothness_mean< -2.326622 135 54 M (0.60000000 0.40000000)
## 68) smoothness_worst>=-1.50451 32 3 M (0.90625000 0.09375000) *
## 69) smoothness_worst< -1.50451 103 51 M (0.50485437 0.49514563) *
## 35) smoothness_mean>=-2.326622 15 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.721197 424 202 M (0.52358491 0.47641509)
## 18) compactness_se< -3.734107 402 180 M (0.55223881 0.44776119)
## 36) smoothness_worst< -1.429075 368 154 M (0.58152174 0.41847826)
## 72) smoothness_worst>=-1.446808 35 3 M (0.91428571 0.08571429) *
## 73) smoothness_worst< -1.446808 333 151 M (0.54654655 0.45345345) *
## 37) smoothness_worst>=-1.429075 34 8 B (0.23529412 0.76470588)
## 74) smoothness_mean>=-2.15812 3 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean< -2.15812 31 5 B (0.16129032 0.83870968) *
## 19) compactness_se>=-3.734107 22 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.363268 19 3 B (0.15789474 0.84210526)
## 10) compactness_se< -3.488238 5 2 M (0.60000000 0.40000000)
## 20) texture_mean>=2.688296 3 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-3.488238 14 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.052205 146 48 B (0.32876712 0.67123288)
## 6) texture_worst< 5.15236 119 48 B (0.40336134 0.59663866)
## 12) symmetry_worst< -2.111279 97 47 B (0.48453608 0.51546392)
## 24) symmetry_worst>=-2.191305 32 8 M (0.75000000 0.25000000)
## 48) smoothness_mean>=-2.408231 25 1 M (0.96000000 0.04000000)
## 96) compactness_se< -3.495845 23 0 M (1.00000000 0.00000000) *
## 97) compactness_se>=-3.495845 2 1 M (0.50000000 0.50000000) *
## 49) smoothness_mean< -2.408231 7 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -2.191305 65 23 B (0.35384615 0.64615385)
## 50) smoothness_mean>=-2.225534 6 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.225534 59 17 B (0.28813559 0.71186441)
## 102) compactness_se< -4.564659 8 1 M (0.87500000 0.12500000) *
## 103) compactness_se>=-4.564659 51 10 B (0.19607843 0.80392157) *
## 13) symmetry_worst>=-2.111279 22 1 B (0.04545455 0.95454545)
## 26) smoothness_mean< -2.576965 1 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.576965 21 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=5.15236 27 0 B (0.00000000 1.00000000) *
##
## $trees[[44]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 431 B (0.47258772 0.52741228)
## 2) symmetry_worst>=-1.36527 34 3 M (0.91176471 0.08823529)
## 4) smoothness_worst>=-1.49848 25 0 M (1.00000000 0.00000000) *
## 5) smoothness_worst< -1.49848 9 3 M (0.66666667 0.33333333)
## 10) smoothness_mean< -2.372457 6 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.372457 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.36527 878 400 B (0.45558087 0.54441913)
## 6) compactness_se< -3.355844 760 366 B (0.48157895 0.51842105)
## 12) texture_worst< 4.740988 495 230 M (0.53535354 0.46464646)
## 24) texture_worst>=4.737862 26 0 M (1.00000000 0.00000000) *
## 25) texture_worst< 4.737862 469 230 M (0.50959488 0.49040512)
## 50) compactness_se>=-3.705619 178 62 M (0.65168539 0.34831461)
## 100) smoothness_worst< -1.477785 136 30 M (0.77941176 0.22058824) *
## 101) smoothness_worst>=-1.477785 42 10 B (0.23809524 0.76190476) *
## 51) compactness_se< -3.705619 291 123 B (0.42268041 0.57731959)
## 102) smoothness_worst>=-1.451541 42 9 M (0.78571429 0.21428571) *
## 103) smoothness_worst< -1.451541 249 90 B (0.36144578 0.63855422) *
## 13) texture_worst>=4.740988 265 101 B (0.38113208 0.61886792)
## 26) smoothness_worst>=-1.52112 122 52 M (0.57377049 0.42622951)
## 52) texture_worst>=4.821213 101 33 M (0.67326733 0.32673267)
## 104) texture_worst< 5.163886 71 12 M (0.83098592 0.16901408) *
## 105) texture_worst>=5.163886 30 9 B (0.30000000 0.70000000) *
## 53) texture_worst< 4.821213 21 2 B (0.09523810 0.90476190)
## 106) smoothness_mean>=-2.208092 2 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.208092 19 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.52112 143 31 B (0.21678322 0.78321678)
## 54) compactness_se< -4.49816 25 10 M (0.60000000 0.40000000)
## 108) compactness_se>=-4.706178 16 1 M (0.93750000 0.06250000) *
## 109) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-4.49816 118 16 B (0.13559322 0.86440678)
## 110) smoothness_mean>=-2.369574 34 14 B (0.41176471 0.58823529) *
## 111) smoothness_mean< -2.369574 84 2 B (0.02380952 0.97619048) *
## 7) compactness_se>=-3.355844 118 34 B (0.28813559 0.71186441)
## 14) texture_mean>=3.039251 38 11 M (0.71052632 0.28947368)
## 28) texture_worst< 4.980233 21 1 M (0.95238095 0.04761905)
## 56) smoothness_mean>=-2.479915 19 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.479915 2 1 M (0.50000000 0.50000000)
## 114) texture_mean>=3.072866 1 0 M (1.00000000 0.00000000) *
## 115) texture_mean< 3.072866 1 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.980233 17 7 B (0.41176471 0.58823529)
## 58) texture_worst>=5.016194 6 0 M (1.00000000 0.00000000) *
## 59) texture_worst< 5.016194 11 1 B (0.09090909 0.90909091)
## 118) texture_mean< 3.154646 1 0 M (1.00000000 0.00000000) *
## 119) texture_mean>=3.154646 10 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.039251 80 7 B (0.08750000 0.91250000)
## 30) smoothness_worst>=-1.387398 7 2 M (0.71428571 0.28571429)
## 60) texture_mean>=2.701935 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.701935 2 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.387398 73 2 B (0.02739726 0.97260274)
## 62) texture_mean>=3.031099 5 1 B (0.20000000 0.80000000)
## 124) texture_mean< 3.032546 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean>=3.032546 4 0 B (0.00000000 1.00000000) *
## 63) texture_mean< 3.031099 68 1 B (0.01470588 0.98529412)
## 126) symmetry_worst>=-1.474719 9 1 B (0.11111111 0.88888889) *
## 127) symmetry_worst< -1.474719 59 0 B (0.00000000 1.00000000) *
##
## $trees[[45]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_worst< 4.642157 551 235 M (0.57350272 0.42649728)
## 4) texture_mean>=2.96604 155 36 M (0.76774194 0.23225806)
## 8) texture_worst>=4.354728 150 31 M (0.79333333 0.20666667)
## 16) compactness_se< -2.807696 141 25 M (0.82269504 0.17730496)
## 32) compactness_se>=-4.291103 127 18 M (0.85826772 0.14173228)
## 64) texture_mean< 3.138519 124 15 M (0.87903226 0.12096774) *
## 65) texture_mean>=3.138519 3 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.291103 14 7 M (0.50000000 0.50000000)
## 66) symmetry_worst< -1.7426 9 2 M (0.77777778 0.22222222) *
## 67) symmetry_worst>=-1.7426 5 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.807696 9 3 B (0.33333333 0.66666667)
## 34) texture_mean>=3.003947 3 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.003947 6 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.354728 5 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.96604 396 197 B (0.49747475 0.50252525)
## 10) smoothness_worst< -1.470556 309 139 M (0.55016181 0.44983819)
## 20) texture_mean< 2.940483 268 106 M (0.60447761 0.39552239)
## 40) compactness_se>=-4.701576 257 95 M (0.63035019 0.36964981)
## 80) smoothness_mean>=-2.50355 248 86 M (0.65322581 0.34677419) *
## 81) smoothness_mean< -2.50355 9 0 B (0.00000000 1.00000000) *
## 41) compactness_se< -4.701576 11 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=2.940483 41 8 B (0.19512195 0.80487805)
## 42) smoothness_worst>=-1.506664 11 3 M (0.72727273 0.27272727)
## 84) texture_mean>=2.955938 8 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 2.955938 3 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst< -1.506664 30 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.470556 87 27 B (0.31034483 0.68965517)
## 22) smoothness_worst>=-1.448697 51 25 M (0.50980392 0.49019608)
## 44) smoothness_worst< -1.440419 18 2 M (0.88888889 0.11111111)
## 88) texture_mean>=2.801549 16 0 M (1.00000000 0.00000000) *
## 89) texture_mean< 2.801549 2 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.440419 33 10 B (0.30303030 0.69696970)
## 90) compactness_se>=-3.300819 5 0 M (1.00000000 0.00000000) *
## 91) compactness_se< -3.300819 28 5 B (0.17857143 0.82142857) *
## 23) smoothness_worst< -1.448697 36 1 B (0.02777778 0.97222222)
## 46) texture_worst< 4.017902 2 1 M (0.50000000 0.50000000)
## 92) texture_mean>=2.587173 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.587173 1 0 B (0.00000000 1.00000000) *
## 47) texture_worst>=4.017902 34 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.642157 361 154 B (0.42659280 0.57340720)
## 6) symmetry_worst>=-1.551105 74 24 M (0.67567568 0.32432432)
## 12) compactness_se>=-4.507761 68 18 M (0.73529412 0.26470588)
## 24) compactness_se< -3.135699 58 11 M (0.81034483 0.18965517)
## 48) texture_worst>=4.82155 27 0 M (1.00000000 0.00000000) *
## 49) texture_worst< 4.82155 31 11 M (0.64516129 0.35483871)
## 98) texture_worst< 4.789782 20 0 M (1.00000000 0.00000000) *
## 99) texture_worst>=4.789782 11 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.135699 10 3 B (0.30000000 0.70000000)
## 50) smoothness_mean>=-2.336585 3 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.336585 7 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.507761 6 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.551105 287 104 B (0.36236934 0.63763066)
## 14) compactness_se< -4.434687 31 8 M (0.74193548 0.25806452)
## 28) symmetry_worst>=-1.912217 23 2 M (0.91304348 0.08695652)
## 56) texture_mean>=2.915217 21 0 M (1.00000000 0.00000000) *
## 57) texture_mean< 2.915217 2 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.912217 8 2 B (0.25000000 0.75000000)
## 58) smoothness_worst>=-1.555243 3 1 M (0.66666667 0.33333333)
## 116) texture_mean< 3.213341 2 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.213341 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.555243 5 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.434687 256 81 B (0.31640625 0.68359375)
## 30) smoothness_mean>=-2.394379 176 69 B (0.39204545 0.60795455)
## 60) smoothness_worst< -1.484675 83 34 M (0.59036145 0.40963855)
## 120) symmetry_worst>=-2.207988 67 18 M (0.73134328 0.26865672) *
## 121) symmetry_worst< -2.207988 16 0 B (0.00000000 1.00000000) *
## 61) smoothness_worst>=-1.484675 93 20 B (0.21505376 0.78494624)
## 122) compactness_se>=-3.300427 7 0 M (1.00000000 0.00000000) *
## 123) compactness_se< -3.300427 86 13 B (0.15116279 0.84883721) *
## 31) smoothness_mean< -2.394379 80 12 B (0.15000000 0.85000000)
## 62) smoothness_worst>=-1.522574 20 9 B (0.45000000 0.55000000)
## 124) smoothness_mean< -2.439503 11 2 M (0.81818182 0.18181818) *
## 125) smoothness_mean>=-2.439503 9 0 B (0.00000000 1.00000000) *
## 63) smoothness_worst< -1.522574 60 3 B (0.05000000 0.95000000)
## 126) symmetry_worst< -2.242858 1 0 M (1.00000000 0.00000000) *
## 127) symmetry_worst>=-2.242858 59 2 B (0.03389831 0.96610169) *
##
## $trees[[46]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 B (0.46600877 0.53399123)
## 2) smoothness_mean>=-2.367658 500 235 M (0.53000000 0.47000000)
## 4) smoothness_mean< -2.349943 56 8 M (0.85714286 0.14285714)
## 8) symmetry_worst< -1.528454 49 1 M (0.97959184 0.02040816)
## 16) compactness_se>=-4.721249 48 0 M (1.00000000 0.00000000) *
## 17) compactness_se< -4.721249 1 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.528454 7 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.349943 444 217 B (0.48873874 0.51126126)
## 10) compactness_se>=-4.025757 329 141 M (0.57142857 0.42857143)
## 20) compactness_se< -3.494301 211 65 M (0.69194313 0.30805687)
## 40) texture_worst>=4.907333 47 2 M (0.95744681 0.04255319)
## 80) symmetry_worst>=-2.207988 45 0 M (1.00000000 0.00000000) *
## 81) symmetry_worst< -2.207988 2 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.907333 164 63 M (0.61585366 0.38414634)
## 82) texture_worst< 4.608306 139 43 M (0.69064748 0.30935252) *
## 83) texture_worst>=4.608306 25 5 B (0.20000000 0.80000000) *
## 21) compactness_se>=-3.494301 118 42 B (0.35593220 0.64406780)
## 42) compactness_se>=-3.445472 87 42 B (0.48275862 0.51724138)
## 84) texture_worst>=4.558285 30 7 M (0.76666667 0.23333333) *
## 85) texture_worst< 4.558285 57 19 B (0.33333333 0.66666667) *
## 43) compactness_se< -3.445472 31 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.025757 115 29 B (0.25217391 0.74782609)
## 22) smoothness_mean< -2.290664 41 13 M (0.68292683 0.31707317)
## 44) smoothness_mean>=-2.333927 34 6 M (0.82352941 0.17647059)
## 88) compactness_se>=-4.656191 31 3 M (0.90322581 0.09677419) *
## 89) compactness_se< -4.656191 3 0 B (0.00000000 1.00000000) *
## 45) smoothness_mean< -2.333927 7 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.290664 74 1 B (0.01351351 0.98648649)
## 46) smoothness_mean>=-2.21595 16 1 B (0.06250000 0.93750000)
## 92) smoothness_mean< -2.210016 2 1 M (0.50000000 0.50000000) *
## 93) smoothness_mean>=-2.210016 14 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean< -2.21595 58 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.367658 412 160 B (0.38834951 0.61165049)
## 6) compactness_se< -4.098964 197 94 M (0.52284264 0.47715736)
## 12) compactness_se>=-4.260936 51 4 M (0.92156863 0.07843137)
## 24) smoothness_worst>=-1.667778 49 2 M (0.95918367 0.04081633)
## 48) smoothness_worst< -1.458133 48 1 M (0.97916667 0.02083333)
## 96) smoothness_mean>=-2.440377 41 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.440377 7 1 M (0.85714286 0.14285714) *
## 49) smoothness_worst>=-1.458133 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.667778 2 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.260936 146 56 B (0.38356164 0.61643836)
## 26) compactness_se< -4.658767 28 6 M (0.78571429 0.21428571)
## 52) compactness_se>=-4.737326 22 0 M (1.00000000 0.00000000) *
## 53) compactness_se< -4.737326 6 0 B (0.00000000 1.00000000) *
## 27) compactness_se>=-4.658767 118 34 B (0.28813559 0.71186441)
## 54) compactness_se>=-4.356557 53 26 B (0.49056604 0.50943396)
## 108) smoothness_mean< -2.45841 24 3 M (0.87500000 0.12500000) *
## 109) smoothness_mean>=-2.45841 29 5 B (0.17241379 0.82758621) *
## 55) compactness_se< -4.356557 65 8 B (0.12307692 0.87692308)
## 110) texture_mean>=2.985131 30 8 B (0.26666667 0.73333333) *
## 111) texture_mean< 2.985131 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-4.098964 215 57 B (0.26511628 0.73488372)
## 14) texture_worst>=4.569119 116 46 B (0.39655172 0.60344828)
## 28) texture_worst< 4.683744 43 13 M (0.69767442 0.30232558)
## 56) smoothness_worst< -1.532274 26 3 M (0.88461538 0.11538462)
## 112) texture_mean< 3.146714 24 1 M (0.95833333 0.04166667) *
## 113) texture_mean>=3.146714 2 0 B (0.00000000 1.00000000) *
## 57) smoothness_worst>=-1.532274 17 7 B (0.41176471 0.58823529)
## 114) texture_mean>=2.955415 7 0 M (1.00000000 0.00000000) *
## 115) texture_mean< 2.955415 10 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.683744 73 16 B (0.21917808 0.78082192)
## 58) texture_mean>=3.428781 3 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.428781 70 13 B (0.18571429 0.81428571)
## 118) symmetry_worst>=-1.541072 5 1 M (0.80000000 0.20000000) *
## 119) symmetry_worst< -1.541072 65 9 B (0.13846154 0.86153846) *
## 15) texture_worst< 4.569119 99 11 B (0.11111111 0.88888889)
## 30) smoothness_mean>=-2.376139 4 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.376139 95 7 B (0.07368421 0.92631579)
## 62) smoothness_worst>=-1.455747 2 0 M (1.00000000 0.00000000) *
## 63) smoothness_worst< -1.455747 93 5 B (0.05376344 0.94623656)
## 126) texture_worst< 3.981964 11 4 B (0.36363636 0.63636364) *
## 127) texture_worst>=3.981964 82 1 B (0.01219512 0.98780488) *
##
## $trees[[47]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 B (0.47368421 0.52631579)
## 2) texture_mean>=2.960364 442 186 M (0.57918552 0.42081448)
## 4) smoothness_mean< -2.106736 422 168 M (0.60189573 0.39810427)
## 8) compactness_se>=-4.275512 330 116 M (0.64848485 0.35151515)
## 16) smoothness_worst>=-1.618016 308 100 M (0.67532468 0.32467532)
## 32) compactness_se< -4.096569 32 1 M (0.96875000 0.03125000)
## 64) symmetry_worst>=-2.253809 31 0 M (1.00000000 0.00000000) *
## 65) symmetry_worst< -2.253809 1 0 B (0.00000000 1.00000000) *
## 33) compactness_se>=-4.096569 276 99 M (0.64130435 0.35869565)
## 66) compactness_se>=-4.05446 262 85 M (0.67557252 0.32442748) *
## 67) compactness_se< -4.05446 14 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.618016 22 6 B (0.27272727 0.72727273)
## 34) compactness_se>=-3.004445 7 2 M (0.71428571 0.28571429)
## 68) texture_mean>=3.038737 5 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 3.038737 2 0 B (0.00000000 1.00000000) *
## 35) compactness_se< -3.004445 15 1 B (0.06666667 0.93333333)
## 70) compactness_se< -4.171292 1 0 M (1.00000000 0.00000000) *
## 71) compactness_se>=-4.171292 14 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.275512 92 40 B (0.43478261 0.56521739)
## 18) smoothness_mean< -2.40097 51 17 M (0.66666667 0.33333333)
## 36) symmetry_worst>=-1.953246 37 5 M (0.86486486 0.13513514)
## 72) texture_mean< 3.192081 34 2 M (0.94117647 0.05882353) *
## 73) texture_mean>=3.192081 3 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.953246 14 2 B (0.14285714 0.85714286)
## 74) smoothness_worst>=-1.552639 2 0 M (1.00000000 0.00000000) *
## 75) smoothness_worst< -1.552639 12 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.40097 41 6 B (0.14634146 0.85365854)
## 38) smoothness_worst< -1.628375 2 0 M (1.00000000 0.00000000) *
## 39) smoothness_worst>=-1.628375 39 4 B (0.10256410 0.89743590)
## 78) smoothness_worst>=-1.433001 1 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.433001 38 3 B (0.07894737 0.92105263) *
## 5) smoothness_mean>=-2.106736 20 2 B (0.10000000 0.90000000)
## 10) smoothness_mean>=-2.05387 2 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean< -2.05387 18 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.960364 470 176 B (0.37446809 0.62553191)
## 6) symmetry_worst>=-1.348749 24 4 M (0.83333333 0.16666667)
## 12) compactness_se< -2.588521 22 2 M (0.90909091 0.09090909)
## 24) smoothness_mean>=-2.360133 21 1 M (0.95238095 0.04761905)
## 48) smoothness_mean< -2.022167 20 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.022167 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.360133 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-2.588521 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.348749 446 156 B (0.34977578 0.65022422)
## 14) texture_mean>=2.708379 400 153 B (0.38250000 0.61750000)
## 28) symmetry_worst< -1.707562 242 113 B (0.46694215 0.53305785)
## 56) texture_worst< 4.54138 175 78 M (0.55428571 0.44571429)
## 112) texture_worst>=4.507583 36 3 M (0.91666667 0.08333333) *
## 113) texture_worst< 4.507583 139 64 B (0.46043165 0.53956835) *
## 57) texture_worst>=4.54138 67 16 B (0.23880597 0.76119403)
## 114) smoothness_mean< -2.350326 27 11 M (0.59259259 0.40740741) *
## 115) smoothness_mean>=-2.350326 40 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst>=-1.707562 158 40 B (0.25316456 0.74683544)
## 58) smoothness_mean>=-2.177741 21 3 M (0.85714286 0.14285714)
## 116) texture_worst>=4.148692 18 0 M (1.00000000 0.00000000) *
## 117) texture_worst< 4.148692 3 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.177741 137 22 B (0.16058394 0.83941606)
## 118) smoothness_worst>=-1.482509 50 17 B (0.34000000 0.66000000) *
## 119) smoothness_worst< -1.482509 87 5 B (0.05747126 0.94252874) *
## 15) texture_mean< 2.708379 46 3 B (0.06521739 0.93478261)
## 30) smoothness_mean>=-2.074653 7 3 B (0.42857143 0.57142857)
## 60) smoothness_mean< -2.060513 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.060513 4 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.074653 39 0 B (0.00000000 1.00000000) *
##
## $trees[[48]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 B (0.47149123 0.52850877)
## 2) texture_mean>=2.708379 870 425 B (0.48850575 0.51149425)
## 4) texture_worst< 4.467083 259 96 M (0.62934363 0.37065637)
## 8) smoothness_mean< -2.262885 205 60 M (0.70731707 0.29268293)
## 16) smoothness_worst>=-1.637109 193 48 M (0.75129534 0.24870466)
## 32) texture_worst>=4.254671 131 21 M (0.83969466 0.16030534)
## 64) symmetry_worst< -1.403642 127 17 M (0.86614173 0.13385827) *
## 65) symmetry_worst>=-1.403642 4 0 B (0.00000000 1.00000000) *
## 33) texture_worst< 4.254671 62 27 M (0.56451613 0.43548387)
## 66) texture_worst< 4.190306 49 14 M (0.71428571 0.28571429) *
## 67) texture_worst>=4.190306 13 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.637109 12 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.262885 54 18 B (0.33333333 0.66666667)
## 18) smoothness_mean>=-2.175971 24 7 M (0.70833333 0.29166667)
## 36) compactness_se>=-3.668604 18 1 M (0.94444444 0.05555556)
## 72) texture_worst>=3.952268 17 0 M (1.00000000 0.00000000) *
## 73) texture_worst< 3.952268 1 0 B (0.00000000 1.00000000) *
## 37) compactness_se< -3.668604 6 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.175971 30 1 B (0.03333333 0.96666667)
## 38) texture_worst< 4.036973 1 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.036973 29 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.467083 611 262 B (0.42880524 0.57119476)
## 10) symmetry_worst>=-1.549706 115 45 M (0.60869565 0.39130435)
## 20) compactness_se< -3.180898 105 36 M (0.65714286 0.34285714)
## 40) texture_worst>=4.61159 75 17 M (0.77333333 0.22666667)
## 80) compactness_se>=-4.694501 72 14 M (0.80555556 0.19444444) *
## 81) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.61159 30 11 B (0.36666667 0.63333333)
## 82) texture_mean>=2.950145 13 2 M (0.84615385 0.15384615) *
## 83) texture_mean< 2.950145 17 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-3.180898 10 1 B (0.10000000 0.90000000)
## 42) smoothness_mean>=-2.346429 1 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.346429 9 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.549706 496 192 B (0.38709677 0.61290323)
## 22) texture_mean>=2.929857 400 173 B (0.43250000 0.56750000)
## 44) symmetry_worst< -1.606972 355 167 B (0.47042254 0.52957746)
## 88) smoothness_worst< -1.438548 319 157 M (0.50783699 0.49216301) *
## 89) smoothness_worst>=-1.438548 36 5 B (0.13888889 0.86111111) *
## 45) symmetry_worst>=-1.606972 45 6 B (0.13333333 0.86666667)
## 90) texture_worst< 4.581608 4 0 M (1.00000000 0.00000000) *
## 91) texture_worst>=4.581608 41 2 B (0.04878049 0.95121951) *
## 23) texture_mean< 2.929857 96 19 B (0.19791667 0.80208333)
## 46) texture_worst< 4.545141 36 15 B (0.41666667 0.58333333)
## 92) texture_worst>=4.508732 16 1 M (0.93750000 0.06250000) *
## 93) texture_worst< 4.508732 20 0 B (0.00000000 1.00000000) *
## 47) texture_worst>=4.545141 60 4 B (0.06666667 0.93333333)
## 94) compactness_se< -4.391048 10 4 B (0.40000000 0.60000000) *
## 95) compactness_se>=-4.391048 50 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.708379 42 5 B (0.11904762 0.88095238)
## 6) symmetry_worst>=-1.552505 13 5 B (0.38461538 0.61538462)
## 12) texture_mean< 2.518783 4 0 M (1.00000000 0.00000000) *
## 13) texture_mean>=2.518783 9 1 B (0.11111111 0.88888889)
## 26) compactness_se>=-3.3026 1 0 M (1.00000000 0.00000000) *
## 27) compactness_se< -3.3026 8 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.552505 29 0 B (0.00000000 1.00000000) *
##
## $trees[[49]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 363 B (0.39802632 0.60197368)
## 2) smoothness_worst>=-1.501069 361 176 B (0.48753463 0.51246537)
## 4) smoothness_worst< -1.476605 126 46 M (0.63492063 0.36507937)
## 8) smoothness_worst>=-1.482699 47 5 M (0.89361702 0.10638298)
## 16) texture_worst>=4.136746 45 3 M (0.93333333 0.06666667)
## 32) smoothness_mean< -2.241789 36 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.241789 9 3 M (0.66666667 0.33333333)
## 66) texture_mean>=2.858739 6 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.858739 3 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.136746 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482699 79 38 B (0.48101266 0.51898734)
## 18) smoothness_worst< -1.49223 41 11 M (0.73170732 0.26829268)
## 36) symmetry_worst< -1.456355 37 7 M (0.81081081 0.18918919)
## 72) smoothness_mean>=-2.374383 33 4 M (0.87878788 0.12121212) *
## 73) smoothness_mean< -2.374383 4 1 B (0.25000000 0.75000000) *
## 37) symmetry_worst>=-1.456355 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.49223 38 8 B (0.21052632 0.78947368)
## 38) symmetry_worst>=-1.413975 3 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.413975 35 5 B (0.14285714 0.85714286)
## 78) texture_mean>=3.407548 2 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.407548 33 3 B (0.09090909 0.90909091) *
## 5) smoothness_worst>=-1.476605 235 96 B (0.40851064 0.59148936)
## 10) smoothness_worst>=-1.473476 193 95 B (0.49222798 0.50777202)
## 20) smoothness_mean< -2.300091 52 13 M (0.75000000 0.25000000)
## 40) smoothness_mean>=-2.362601 33 2 M (0.93939394 0.06060606)
## 80) compactness_se>=-4.494315 31 0 M (1.00000000 0.00000000) *
## 81) compactness_se< -4.494315 2 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean< -2.362601 19 8 B (0.42105263 0.57894737)
## 82) symmetry_worst>=-1.446218 7 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst< -1.446218 12 1 B (0.08333333 0.91666667) *
## 21) smoothness_mean>=-2.300091 141 56 B (0.39716312 0.60283688)
## 42) compactness_se>=-4.02632 111 54 B (0.48648649 0.51351351)
## 84) smoothness_mean>=-2.288684 89 35 M (0.60674157 0.39325843) *
## 85) smoothness_mean< -2.288684 22 0 B (0.00000000 1.00000000) *
## 43) compactness_se< -4.02632 30 2 B (0.06666667 0.93333333)
## 86) symmetry_worst< -1.743442 5 2 B (0.40000000 0.60000000) *
## 87) symmetry_worst>=-1.743442 25 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473476 42 1 B (0.02380952 0.97619048)
## 22) texture_mean>=3.069079 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.069079 41 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.501069 551 187 B (0.33938294 0.66061706)
## 6) texture_worst< 4.467472 160 77 M (0.51875000 0.48125000)
## 12) compactness_se< -3.48221 132 51 M (0.61363636 0.38636364)
## 24) texture_worst>=3.891616 118 37 M (0.68644068 0.31355932)
## 48) compactness_se>=-4.519704 102 23 M (0.77450980 0.22549020)
## 96) symmetry_worst< -1.576447 95 16 M (0.83157895 0.16842105) *
## 97) symmetry_worst>=-1.576447 7 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.519704 16 2 B (0.12500000 0.87500000)
## 98) smoothness_mean>=-2.306694 2 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean< -2.306694 14 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.891616 14 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.48221 28 2 B (0.07142857 0.92857143)
## 26) texture_worst>=4.411908 2 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.411908 26 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=4.467472 391 104 B (0.26598465 0.73401535)
## 14) compactness_se>=-3.005655 20 6 M (0.70000000 0.30000000)
## 28) texture_mean>=3.058386 14 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 3.058386 6 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.005655 371 90 B (0.24258760 0.75741240)
## 30) compactness_se< -4.032373 140 55 B (0.39285714 0.60714286)
## 60) texture_worst>=5.149489 19 3 M (0.84210526 0.15789474)
## 120) symmetry_worst>=-2.036673 16 0 M (1.00000000 0.00000000) *
## 121) symmetry_worst< -2.036673 3 0 B (0.00000000 1.00000000) *
## 61) texture_worst< 5.149489 121 39 B (0.32231405 0.67768595)
## 122) smoothness_worst< -1.607486 21 6 M (0.71428571 0.28571429) *
## 123) smoothness_worst>=-1.607486 100 24 B (0.24000000 0.76000000) *
## 31) compactness_se>=-4.032373 231 35 B (0.15151515 0.84848485)
## 62) symmetry_worst>=-1.789477 91 24 B (0.26373626 0.73626374)
## 124) symmetry_worst< -1.767566 8 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.767566 83 16 B (0.19277108 0.80722892) *
## 63) symmetry_worst< -1.789477 140 11 B (0.07857143 0.92142857)
## 126) texture_mean>=3.313386 9 4 M (0.55555556 0.44444444) *
## 127) texture_mean< 3.313386 131 6 B (0.04580153 0.95419847) *
##
## $trees[[50]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 372 B (0.40789474 0.59210526)
## 2) symmetry_worst>=-1.775265 446 215 M (0.51793722 0.48206278)
## 4) texture_mean>=2.920739 316 125 M (0.60443038 0.39556962)
## 8) texture_worst< 4.930927 249 82 M (0.67068273 0.32931727)
## 16) compactness_se< -4.07887 78 9 M (0.88461538 0.11538462)
## 32) symmetry_worst< -1.51291 64 2 M (0.96875000 0.03125000)
## 64) smoothness_mean< -2.306529 59 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.306529 5 2 M (0.60000000 0.40000000) *
## 33) symmetry_worst>=-1.51291 14 7 M (0.50000000 0.50000000)
## 66) texture_mean>=2.99247 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.99247 7 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-4.07887 171 73 M (0.57309942 0.42690058)
## 34) compactness_se>=-3.681558 102 26 M (0.74509804 0.25490196)
## 68) compactness_se< -3.502612 41 0 M (1.00000000 0.00000000) *
## 69) compactness_se>=-3.502612 61 26 M (0.57377049 0.42622951) *
## 35) compactness_se< -3.681558 69 22 B (0.31884058 0.68115942)
## 70) smoothness_worst>=-1.462341 24 6 M (0.75000000 0.25000000) *
## 71) smoothness_worst< -1.462341 45 4 B (0.08888889 0.91111111) *
## 9) texture_worst>=4.930927 67 24 B (0.35820896 0.64179104)
## 18) texture_worst>=5.003123 33 11 M (0.66666667 0.33333333)
## 36) texture_mean< 3.225651 14 0 M (1.00000000 0.00000000) *
## 37) texture_mean>=3.225651 19 8 B (0.42105263 0.57894737)
## 74) smoothness_mean>=-2.329526 5 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean< -2.329526 14 3 B (0.21428571 0.78571429) *
## 19) texture_worst< 5.003123 34 2 B (0.05882353 0.94117647)
## 38) symmetry_worst>=-1.450896 1 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.450896 33 1 B (0.03030303 0.96969697)
## 78) texture_mean< 3.088538 4 1 B (0.25000000 0.75000000) *
## 79) texture_mean>=3.088538 29 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.920739 130 40 B (0.30769231 0.69230769)
## 10) texture_mean< 2.850705 92 39 B (0.42391304 0.57608696)
## 20) texture_mean>=2.824054 26 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.824054 66 13 B (0.19696970 0.80303030)
## 42) smoothness_worst>=-1.491834 34 13 B (0.38235294 0.61764706)
## 84) texture_worst>=4.110502 14 5 M (0.64285714 0.35714286) *
## 85) texture_worst< 4.110502 20 4 B (0.20000000 0.80000000) *
## 43) smoothness_worst< -1.491834 32 0 B (0.00000000 1.00000000) *
## 11) texture_mean>=2.850705 38 1 B (0.02631579 0.97368421)
## 22) smoothness_mean>=-2.213964 1 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.213964 37 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.775265 466 141 B (0.30257511 0.69742489)
## 6) smoothness_worst>=-1.603315 406 138 B (0.33990148 0.66009852)
## 12) smoothness_worst< -1.602623 10 0 M (1.00000000 0.00000000) *
## 13) smoothness_worst>=-1.602623 396 128 B (0.32323232 0.67676768)
## 26) compactness_se< -3.869459 175 75 B (0.42857143 0.57142857)
## 52) texture_worst>=4.907333 33 6 M (0.81818182 0.18181818)
## 104) texture_mean< 3.353705 30 3 M (0.90000000 0.10000000) *
## 105) texture_mean>=3.353705 3 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.907333 142 48 B (0.33802817 0.66197183)
## 106) texture_worst< 4.751011 109 48 B (0.44036697 0.55963303) *
## 107) texture_worst>=4.751011 33 0 B (0.00000000 1.00000000) *
## 27) compactness_se>=-3.869459 221 53 B (0.23981900 0.76018100)
## 54) smoothness_mean< -2.473552 12 1 M (0.91666667 0.08333333)
## 108) smoothness_mean>=-2.497829 11 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.497829 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_mean>=-2.473552 209 42 B (0.20095694 0.79904306)
## 110) smoothness_mean>=-2.377849 129 42 B (0.32558140 0.67441860) *
## 111) smoothness_mean< -2.377849 80 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.603315 60 3 B (0.05000000 0.95000000)
## 14) compactness_se>=-2.951614 1 0 M (1.00000000 0.00000000) *
## 15) compactness_se< -2.951614 59 2 B (0.03389831 0.96610169)
## 30) compactness_se< -4.691681 6 2 B (0.33333333 0.66666667)
## 60) compactness_se>=-4.711555 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.711555 4 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.691681 53 0 B (0.00000000 1.00000000) *
##
## $trees[[51]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 388 B (0.42543860 0.57456140)
## 2) texture_mean>=2.853016 706 326 B (0.46175637 0.53824363)
## 4) smoothness_worst>=-1.556752 499 243 M (0.51302605 0.48697395)
## 8) smoothness_mean< -2.349264 199 67 M (0.66331658 0.33668342)
## 16) compactness_se< -3.938851 115 20 M (0.82608696 0.17391304)
## 32) smoothness_mean>=-2.473387 103 8 M (0.92233010 0.07766990)
## 64) symmetry_worst>=-1.959872 83 2 M (0.97590361 0.02409639) *
## 65) symmetry_worst< -1.959872 20 6 M (0.70000000 0.30000000) *
## 33) smoothness_mean< -2.473387 12 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.938851 84 37 B (0.44047619 0.55952381)
## 34) smoothness_mean>=-2.362071 15 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.362071 69 22 B (0.31884058 0.68115942)
## 70) smoothness_mean< -2.461054 12 0 M (1.00000000 0.00000000) *
## 71) smoothness_mean>=-2.461054 57 10 B (0.17543860 0.82456140) *
## 9) smoothness_mean>=-2.349264 300 124 B (0.41333333 0.58666667)
## 18) compactness_se>=-3.219881 34 8 M (0.76470588 0.23529412)
## 36) smoothness_mean>=-2.332581 30 4 M (0.86666667 0.13333333)
## 72) smoothness_mean< -2.224699 21 0 M (1.00000000 0.00000000) *
## 73) smoothness_mean>=-2.224699 9 4 M (0.55555556 0.44444444) *
## 37) smoothness_mean< -2.332581 4 0 B (0.00000000 1.00000000) *
## 19) compactness_se< -3.219881 266 98 B (0.36842105 0.63157895)
## 38) texture_mean< 3.039744 166 76 B (0.45783133 0.54216867)
## 76) compactness_se>=-3.669769 54 15 M (0.72222222 0.27777778) *
## 77) compactness_se< -3.669769 112 37 B (0.33035714 0.66964286) *
## 39) texture_mean>=3.039744 100 22 B (0.22000000 0.78000000)
## 78) texture_worst< 4.664833 5 0 M (1.00000000 0.00000000) *
## 79) texture_worst>=4.664833 95 17 B (0.17894737 0.82105263) *
## 5) smoothness_worst< -1.556752 207 70 B (0.33816425 0.66183575)
## 10) symmetry_worst< -2.242382 22 3 M (0.86363636 0.13636364)
## 20) smoothness_worst>=-1.645584 16 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.645584 6 3 M (0.50000000 0.50000000)
## 42) texture_mean>=3.175045 3 0 M (1.00000000 0.00000000) *
## 43) texture_mean< 3.175045 3 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-2.242382 185 51 B (0.27567568 0.72432432)
## 22) smoothness_worst< -1.568787 127 45 B (0.35433071 0.64566929)
## 44) smoothness_worst>=-1.584838 24 5 M (0.79166667 0.20833333)
## 88) texture_mean>=2.924461 19 0 M (1.00000000 0.00000000) *
## 89) texture_mean< 2.924461 5 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst< -1.584838 103 26 B (0.25242718 0.74757282)
## 90) symmetry_worst>=-1.795801 43 21 B (0.48837209 0.51162791) *
## 91) symmetry_worst< -1.795801 60 5 B (0.08333333 0.91666667) *
## 23) smoothness_worst>=-1.568787 58 6 B (0.10344828 0.89655172)
## 46) smoothness_mean>=-2.299648 2 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.299648 56 4 B (0.07142857 0.92857143)
## 94) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 95) compactness_se< -2.682598 54 2 B (0.03703704 0.96296296) *
## 3) texture_mean< 2.853016 206 62 B (0.30097087 0.69902913)
## 6) smoothness_worst< -1.468426 135 54 B (0.40000000 0.60000000)
## 12) smoothness_mean>=-2.443746 112 54 B (0.48214286 0.51785714)
## 24) texture_mean>=2.656405 99 45 M (0.54545455 0.45454545)
## 48) texture_mean< 2.744378 30 3 M (0.90000000 0.10000000)
## 96) smoothness_mean< -2.205363 28 1 M (0.96428571 0.03571429) *
## 97) smoothness_mean>=-2.205363 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean>=2.744378 69 27 B (0.39130435 0.60869565)
## 98) smoothness_mean< -2.437331 7 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.437331 62 20 B (0.32258065 0.67741935) *
## 25) texture_mean< 2.656405 13 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.443746 23 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.468426 71 8 B (0.11267606 0.88732394)
## 14) texture_worst>=4.398698 3 0 M (1.00000000 0.00000000) *
## 15) texture_worst< 4.398698 68 5 B (0.07352941 0.92647059)
## 30) symmetry_worst>=-1.619683 32 5 B (0.15625000 0.84375000)
## 60) symmetry_worst< -1.483416 6 2 M (0.66666667 0.33333333)
## 120) compactness_se>=-3.961508 4 0 M (1.00000000 0.00000000) *
## 121) compactness_se< -3.961508 2 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst>=-1.483416 26 1 B (0.03846154 0.96153846)
## 122) smoothness_worst< -1.454202 1 0 M (1.00000000 0.00000000) *
## 123) smoothness_worst>=-1.454202 25 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.619683 36 0 B (0.00000000 1.00000000) *
##
## $trees[[52]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 M (0.50877193 0.49122807)
## 2) compactness_se< -3.867535 482 199 M (0.58713693 0.41286307)
## 4) texture_mean>=2.754924 447 164 M (0.63310962 0.36689038)
## 8) compactness_se>=-3.883925 38 0 M (1.00000000 0.00000000) *
## 9) compactness_se< -3.883925 409 164 M (0.59902200 0.40097800)
## 18) symmetry_worst>=-1.966052 318 105 M (0.66981132 0.33018868)
## 36) compactness_se< -3.902076 303 90 M (0.70297030 0.29702970)
## 72) smoothness_mean< -2.300091 240 56 M (0.76666667 0.23333333) *
## 73) smoothness_mean>=-2.300091 63 29 B (0.46031746 0.53968254) *
## 37) compactness_se>=-3.902076 15 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.966052 91 32 B (0.35164835 0.64835165)
## 38) symmetry_worst< -2.170754 31 10 M (0.67741935 0.32258065)
## 76) smoothness_mean>=-2.392268 24 3 M (0.87500000 0.12500000) *
## 77) smoothness_mean< -2.392268 7 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst>=-2.170754 60 11 B (0.18333333 0.81666667)
## 78) smoothness_worst>=-1.525709 15 5 M (0.66666667 0.33333333) *
## 79) smoothness_worst< -1.525709 45 1 B (0.02222222 0.97777778) *
## 5) texture_mean< 2.754924 35 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-3.867535 430 181 B (0.42093023 0.57906977)
## 6) compactness_se>=-3.721197 345 165 B (0.47826087 0.52173913)
## 12) compactness_se< -3.575734 77 21 M (0.72727273 0.27272727)
## 24) smoothness_mean>=-2.423737 53 6 M (0.88679245 0.11320755)
## 48) texture_mean>=2.647471 50 3 M (0.94000000 0.06000000)
## 96) symmetry_worst>=-2.174989 44 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst< -2.174989 6 3 M (0.50000000 0.50000000) *
## 49) texture_mean< 2.647471 3 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.423737 24 9 B (0.37500000 0.62500000)
## 50) symmetry_worst>=-1.813961 10 1 M (0.90000000 0.10000000)
## 100) smoothness_mean< -2.452956 9 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean>=-2.452956 1 0 B (0.00000000 1.00000000) *
## 51) symmetry_worst< -1.813961 14 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.575734 268 109 B (0.40671642 0.59328358)
## 26) texture_mean>=3.059388 81 29 M (0.64197531 0.35802469)
## 52) texture_worst< 4.745147 17 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=4.745147 64 29 M (0.54687500 0.45312500)
## 106) texture_worst>=5.016194 23 5 M (0.78260870 0.21739130) *
## 107) texture_worst< 5.016194 41 17 B (0.41463415 0.58536585) *
## 27) texture_mean< 3.059388 187 57 B (0.30481283 0.69518717)
## 54) smoothness_worst>=-1.502084 87 43 M (0.50574713 0.49425287)
## 108) smoothness_worst< -1.476605 30 5 M (0.83333333 0.16666667) *
## 109) smoothness_worst>=-1.476605 57 19 B (0.33333333 0.66666667) *
## 55) smoothness_worst< -1.502084 100 13 B (0.13000000 0.87000000)
## 110) texture_mean< 2.782752 15 6 M (0.60000000 0.40000000) *
## 111) texture_mean>=2.782752 85 4 B (0.04705882 0.95294118) *
## 7) compactness_se< -3.721197 85 16 B (0.18823529 0.81176471)
## 14) smoothness_worst>=-1.48132 27 11 M (0.59259259 0.40740741)
## 28) texture_mean>=2.971675 12 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.971675 15 4 B (0.26666667 0.73333333)
## 58) symmetry_worst>=-1.612049 4 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.612049 11 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.48132 58 0 B (0.00000000 1.00000000) *
##
## $trees[[53]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) smoothness_worst>=-1.559144 682 299 M (0.56158358 0.43841642)
## 4) symmetry_worst>=-2.233349 648 269 M (0.58487654 0.41512346)
## 8) smoothness_mean< -2.26529 479 174 M (0.63674322 0.36325678)
## 16) smoothness_mean>=-2.416986 388 125 M (0.67783505 0.32216495)
## 32) symmetry_worst>=-2.207519 379 116 M (0.69393140 0.30606860)
## 64) texture_mean< 3.36829 370 108 M (0.70810811 0.29189189) *
## 65) texture_mean>=3.36829 9 1 B (0.11111111 0.88888889) *
## 33) symmetry_worst< -2.207519 9 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.416986 91 42 B (0.46153846 0.53846154)
## 34) smoothness_worst< -1.551775 35 7 M (0.80000000 0.20000000)
## 68) texture_mean>=2.859755 31 3 M (0.90322581 0.09677419) *
## 69) texture_mean< 2.859755 4 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.551775 56 14 B (0.25000000 0.75000000)
## 70) texture_mean>=3.111958 22 8 M (0.63636364 0.36363636) *
## 71) texture_mean< 3.111958 34 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.26529 169 74 B (0.43786982 0.56213018)
## 18) smoothness_mean>=-2.222851 92 38 M (0.58695652 0.41304348)
## 36) smoothness_mean< -2.093138 73 23 M (0.68493151 0.31506849)
## 72) compactness_se>=-3.673673 31 1 M (0.96774194 0.03225806) *
## 73) compactness_se< -3.673673 42 20 B (0.47619048 0.52380952) *
## 37) smoothness_mean>=-2.093138 19 4 B (0.21052632 0.78947368)
## 74) symmetry_worst>=-1.596878 7 3 M (0.57142857 0.42857143) *
## 75) symmetry_worst< -1.596878 12 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.222851 77 20 B (0.25974026 0.74025974)
## 38) smoothness_mean< -2.235394 41 18 B (0.43902439 0.56097561)
## 76) smoothness_worst>=-1.45841 21 5 M (0.76190476 0.23809524) *
## 77) smoothness_worst< -1.45841 20 2 B (0.10000000 0.90000000) *
## 39) smoothness_mean>=-2.235394 36 2 B (0.05555556 0.94444444)
## 78) texture_mean< 2.693961 2 0 M (1.00000000 0.00000000) *
## 79) texture_mean>=2.693961 34 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -2.233349 34 4 B (0.11764706 0.88235294)
## 10) compactness_se>=-2.833542 4 0 M (1.00000000 0.00000000) *
## 11) compactness_se< -2.833542 30 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.559144 230 87 B (0.37826087 0.62173913)
## 6) smoothness_worst< -1.565486 196 86 B (0.43877551 0.56122449)
## 12) texture_worst>=4.354728 158 79 M (0.50000000 0.50000000)
## 24) symmetry_worst>=-1.720706 56 17 M (0.69642857 0.30357143)
## 48) texture_mean>=2.958609 48 9 M (0.81250000 0.18750000)
## 96) smoothness_worst>=-1.660611 45 6 M (0.86666667 0.13333333) *
## 97) smoothness_worst< -1.660611 3 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.958609 8 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.720706 102 40 B (0.39215686 0.60784314)
## 50) compactness_se>=-4.104699 50 20 M (0.60000000 0.40000000)
## 100) texture_worst>=4.56463 33 5 M (0.84848485 0.15151515) *
## 101) texture_worst< 4.56463 17 2 B (0.11764706 0.88235294) *
## 51) compactness_se< -4.104699 52 10 B (0.19230769 0.80769231)
## 102) symmetry_worst< -2.382417 8 0 M (1.00000000 0.00000000) *
## 103) symmetry_worst>=-2.382417 44 2 B (0.04545455 0.95454545) *
## 13) texture_worst< 4.354728 38 7 B (0.18421053 0.81578947)
## 26) smoothness_mean>=-2.30797 5 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean< -2.30797 33 2 B (0.06060606 0.93939394)
## 54) texture_worst< 3.948691 5 2 B (0.40000000 0.60000000)
## 108) texture_mean>=2.754513 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.754513 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=3.948691 28 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.565486 34 1 B (0.02941176 0.97058824)
## 14) smoothness_mean>=-2.299648 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.299648 33 0 B (0.00000000 1.00000000) *
##
## $trees[[54]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 389 B (0.42653509 0.57346491)
## 2) texture_worst>=4.275472 772 357 B (0.46243523 0.53756477)
## 4) symmetry_worst>=-2.041024 641 313 M (0.51170047 0.48829953)
## 8) smoothness_mean>=-2.21595 79 19 M (0.75949367 0.24050633)
## 16) symmetry_worst>=-1.766269 52 3 M (0.94230769 0.05769231)
## 32) texture_mean< 3.039982 42 0 M (1.00000000 0.00000000) *
## 33) texture_mean>=3.039982 10 3 M (0.70000000 0.30000000)
## 66) texture_mean>=3.045947 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.045947 3 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.766269 27 11 B (0.40740741 0.59259259)
## 34) symmetry_worst< -1.891461 10 1 M (0.90000000 0.10000000)
## 68) texture_mean>=2.967292 9 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.967292 1 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst>=-1.891461 17 2 B (0.11764706 0.88235294)
## 70) compactness_se>=-3.317826 2 0 M (1.00000000 0.00000000) *
## 71) compactness_se< -3.317826 15 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.21595 562 268 B (0.47686833 0.52313167)
## 18) texture_worst< 4.280533 24 2 M (0.91666667 0.08333333)
## 36) smoothness_mean>=-2.473491 22 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean< -2.473491 2 0 B (0.00000000 1.00000000) *
## 19) texture_worst>=4.280533 538 246 B (0.45724907 0.54275093)
## 38) texture_worst>=4.517889 412 201 M (0.51213592 0.48786408)
## 76) texture_worst< 4.644679 148 49 M (0.66891892 0.33108108) *
## 77) texture_worst>=4.644679 264 112 B (0.42424242 0.57575758) *
## 39) texture_worst< 4.517889 126 35 B (0.27777778 0.72222222)
## 78) texture_mean>=2.960623 27 5 M (0.81481481 0.18518519) *
## 79) texture_mean< 2.960623 99 13 B (0.13131313 0.86868687) *
## 5) symmetry_worst< -2.041024 131 29 B (0.22137405 0.77862595)
## 10) symmetry_worst< -2.379234 15 4 M (0.73333333 0.26666667)
## 20) texture_mean< 3.283931 12 1 M (0.91666667 0.08333333)
## 40) smoothness_mean< -2.287736 10 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.287736 2 1 M (0.50000000 0.50000000)
## 82) texture_mean>=3.050671 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.050671 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=3.283931 3 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-2.379234 116 18 B (0.15517241 0.84482759)
## 22) compactness_se>=-2.72933 5 1 M (0.80000000 0.20000000)
## 44) texture_mean>=3.063909 4 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.063909 1 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -2.72933 111 14 B (0.12612613 0.87387387)
## 46) smoothness_worst< -1.709211 2 0 M (1.00000000 0.00000000) *
## 47) smoothness_worst>=-1.709211 109 12 B (0.11009174 0.88990826)
## 94) smoothness_worst>=-1.448989 1 0 M (1.00000000 0.00000000) *
## 95) smoothness_worst< -1.448989 108 11 B (0.10185185 0.89814815) *
## 3) texture_worst< 4.275472 140 32 B (0.22857143 0.77142857)
## 6) texture_mean>=2.757473 65 23 B (0.35384615 0.64615385)
## 12) texture_worst< 4.012259 10 0 M (1.00000000 0.00000000) *
## 13) texture_worst>=4.012259 55 13 B (0.23636364 0.76363636)
## 26) texture_mean< 2.760642 7 0 M (1.00000000 0.00000000) *
## 27) texture_mean>=2.760642 48 6 B (0.12500000 0.87500000)
## 54) symmetry_worst>=-1.431518 8 2 M (0.75000000 0.25000000)
## 108) texture_mean< 2.914443 6 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.914443 2 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.431518 40 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.757473 75 9 B (0.12000000 0.88000000)
## 14) texture_mean< 2.715026 38 9 B (0.23684211 0.76315789)
## 28) texture_mean>=2.709047 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.709047 34 5 B (0.14705882 0.85294118)
## 58) texture_mean< 2.515298 6 3 M (0.50000000 0.50000000)
## 116) smoothness_mean< -2.060513 3 0 M (1.00000000 0.00000000) *
## 117) smoothness_mean>=-2.060513 3 0 B (0.00000000 1.00000000) *
## 59) texture_mean>=2.515298 28 2 B (0.07142857 0.92857143)
## 118) smoothness_mean< -2.298096 6 2 B (0.33333333 0.66666667) *
## 119) smoothness_mean>=-2.298096 22 0 B (0.00000000 1.00000000) *
## 15) texture_mean>=2.715026 37 0 B (0.00000000 1.00000000) *
##
## $trees[[55]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) texture_worst>=4.580648 486 191 M (0.60699588 0.39300412)
## 4) smoothness_mean>=-2.408446 368 116 M (0.68478261 0.31521739)
## 8) texture_worst>=4.895983 161 31 M (0.80745342 0.19254658)
## 16) smoothness_mean>=-2.336091 120 10 M (0.91666667 0.08333333)
## 32) smoothness_mean< -2.106736 115 6 M (0.94782609 0.05217391)
## 64) compactness_se>=-4.032549 109 2 M (0.98165138 0.01834862) *
## 65) compactness_se< -4.032549 6 2 B (0.33333333 0.66666667) *
## 33) smoothness_mean>=-2.106736 5 1 B (0.20000000 0.80000000)
## 66) texture_mean< 3.181902 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.181902 4 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.336091 41 20 B (0.48780488 0.51219512)
## 34) smoothness_worst< -1.530302 14 1 M (0.92857143 0.07142857)
## 68) texture_mean< 3.392124 13 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.392124 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.530302 27 7 B (0.25925926 0.74074074)
## 70) texture_worst< 5.113166 6 0 M (1.00000000 0.00000000) *
## 71) texture_worst>=5.113166 21 1 B (0.04761905 0.95238095) *
## 9) texture_worst< 4.895983 207 85 M (0.58937198 0.41062802)
## 18) smoothness_mean< -2.352368 82 10 M (0.87804878 0.12195122)
## 36) texture_worst< 4.876647 76 4 M (0.94736842 0.05263158)
## 72) texture_mean< 3.135225 71 0 M (1.00000000 0.00000000) *
## 73) texture_mean>=3.135225 5 1 B (0.20000000 0.80000000) *
## 37) texture_worst>=4.876647 6 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.352368 125 50 B (0.40000000 0.60000000)
## 38) texture_worst< 4.608306 17 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.608306 108 33 B (0.30555556 0.69444444)
## 78) symmetry_worst>=-1.550826 39 14 M (0.64102564 0.35897436) *
## 79) symmetry_worst< -1.550826 69 8 B (0.11594203 0.88405797) *
## 5) smoothness_mean< -2.408446 118 43 B (0.36440678 0.63559322)
## 10) smoothness_mean< -2.443746 85 39 B (0.45882353 0.54117647)
## 20) smoothness_mean>=-2.489159 45 15 M (0.66666667 0.33333333)
## 40) texture_mean>=2.991714 34 7 M (0.79411765 0.20588235)
## 80) texture_worst< 5.316369 27 2 M (0.92592593 0.07407407) *
## 81) texture_worst>=5.316369 7 2 B (0.28571429 0.71428571) *
## 41) texture_mean< 2.991714 11 3 B (0.27272727 0.72727273)
## 82) smoothness_mean>=-2.457256 3 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.457256 8 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.489159 40 9 B (0.22500000 0.77500000)
## 42) texture_mean< 2.966301 5 0 M (1.00000000 0.00000000) *
## 43) texture_mean>=2.966301 35 4 B (0.11428571 0.88571429)
## 86) symmetry_worst>=-1.695215 11 4 B (0.36363636 0.63636364) *
## 87) symmetry_worst< -1.695215 24 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.443746 33 4 B (0.12121212 0.87878788)
## 22) texture_worst< 4.592462 3 0 M (1.00000000 0.00000000) *
## 23) texture_worst>=4.592462 30 1 B (0.03333333 0.96666667)
## 46) texture_worst>=5.149193 4 1 B (0.25000000 0.75000000)
## 92) texture_mean>=3.032025 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 3.032025 3 0 B (0.00000000 1.00000000) *
## 47) texture_worst< 5.149193 26 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.580648 426 153 B (0.35915493 0.64084507)
## 6) smoothness_worst>=-1.496637 178 87 B (0.48876404 0.51123596)
## 12) smoothness_worst< -1.476801 56 11 M (0.80357143 0.19642857)
## 24) smoothness_mean>=-2.315133 51 6 M (0.88235294 0.11764706)
## 48) smoothness_mean< -2.275944 38 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.275944 13 6 M (0.53846154 0.46153846)
## 98) texture_mean>=3.003189 7 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.003189 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.315133 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.476801 122 42 B (0.34426230 0.65573770)
## 26) smoothness_worst>=-1.451541 54 21 M (0.61111111 0.38888889)
## 52) texture_mean>=2.798684 30 4 M (0.86666667 0.13333333)
## 104) smoothness_mean< -2.155998 24 1 M (0.95833333 0.04166667) *
## 105) smoothness_mean>=-2.155998 6 3 M (0.50000000 0.50000000) *
## 53) texture_mean< 2.798684 24 7 B (0.29166667 0.70833333)
## 106) symmetry_worst>=-1.232339 3 0 M (1.00000000 0.00000000) *
## 107) symmetry_worst< -1.232339 21 4 B (0.19047619 0.80952381) *
## 27) smoothness_worst< -1.451541 68 9 B (0.13235294 0.86764706)
## 54) compactness_se>=-3.453499 12 4 M (0.66666667 0.33333333)
## 108) texture_mean< 2.801271 8 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.801271 4 0 B (0.00000000 1.00000000) *
## 55) compactness_se< -3.453499 56 1 B (0.01785714 0.98214286)
## 110) texture_mean>=2.932513 1 0 M (1.00000000 0.00000000) *
## 111) texture_mean< 2.932513 55 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.496637 248 66 B (0.26612903 0.73387097)
## 14) symmetry_worst>=-1.830253 146 56 B (0.38356164 0.61643836)
## 28) symmetry_worst< -1.598517 113 55 B (0.48672566 0.51327434)
## 56) compactness_se< -3.93685 48 12 M (0.75000000 0.25000000)
## 112) smoothness_mean< -2.320044 43 7 M (0.83720930 0.16279070) *
## 113) smoothness_mean>=-2.320044 5 0 B (0.00000000 1.00000000) *
## 57) compactness_se>=-3.93685 65 19 B (0.29230769 0.70769231)
## 114) smoothness_mean>=-2.384488 23 7 M (0.69565217 0.30434783) *
## 115) smoothness_mean< -2.384488 42 3 B (0.07142857 0.92857143) *
## 29) symmetry_worst>=-1.598517 33 1 B (0.03030303 0.96969697)
## 58) texture_mean>=2.990556 1 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 2.990556 32 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.830253 102 10 B (0.09803922 0.90196078)
## 30) symmetry_worst< -2.880164 4 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst>=-2.880164 98 6 B (0.06122449 0.93877551)
## 62) compactness_se>=-2.988951 2 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -2.988951 96 4 B (0.04166667 0.95833333)
## 126) compactness_se< -4.49319 11 4 B (0.36363636 0.63636364) *
## 127) compactness_se>=-4.49319 85 0 B (0.00000000 1.00000000) *
##
## $trees[[56]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) smoothness_worst>=-1.504307 393 171 M (0.56488550 0.43511450)
## 4) smoothness_worst< -1.476801 130 30 M (0.76923077 0.23076923)
## 8) compactness_se>=-4.245893 107 14 M (0.86915888 0.13084112)
## 16) smoothness_mean>=-2.367658 97 8 M (0.91752577 0.08247423)
## 32) symmetry_worst< -1.561818 77 2 M (0.97402597 0.02597403)
## 64) texture_mean>=2.647471 76 1 M (0.98684211 0.01315789) *
## 65) texture_mean< 2.647471 1 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst>=-1.561818 20 6 M (0.70000000 0.30000000)
## 66) smoothness_worst>=-1.491834 11 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst< -1.491834 9 3 B (0.33333333 0.66666667) *
## 17) smoothness_mean< -2.367658 10 4 B (0.40000000 0.60000000)
## 34) smoothness_mean< -2.408063 5 1 M (0.80000000 0.20000000)
## 68) texture_mean>=2.903025 4 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.903025 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.408063 5 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.245893 23 7 B (0.30434783 0.69565217)
## 18) symmetry_worst< -1.688448 9 2 M (0.77777778 0.22222222)
## 36) texture_mean>=2.800736 7 0 M (1.00000000 0.00000000) *
## 37) texture_mean< 2.800736 2 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst>=-1.688448 14 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.476801 263 122 B (0.46387833 0.53612167)
## 10) smoothness_mean>=-2.225626 67 17 M (0.74626866 0.25373134)
## 20) symmetry_worst>=-1.766269 52 6 M (0.88461538 0.11538462)
## 40) smoothness_worst< -1.396673 36 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst>=-1.396673 16 6 M (0.62500000 0.37500000)
## 82) symmetry_worst>=-1.616162 10 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst< -1.616162 6 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.766269 15 4 B (0.26666667 0.73333333)
## 42) smoothness_worst< -1.465711 4 0 M (1.00000000 0.00000000) *
## 43) smoothness_worst>=-1.465711 11 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.225626 196 72 B (0.36734694 0.63265306)
## 22) texture_worst>=4.866447 58 19 M (0.67241379 0.32758621)
## 44) compactness_se>=-4.512898 47 8 M (0.82978723 0.17021277)
## 88) compactness_se< -2.942351 43 4 M (0.90697674 0.09302326) *
## 89) compactness_se>=-2.942351 4 0 B (0.00000000 1.00000000) *
## 45) compactness_se< -4.512898 11 0 B (0.00000000 1.00000000) *
## 23) texture_worst< 4.866447 138 33 B (0.23913043 0.76086957)
## 46) symmetry_worst>=-1.864441 104 33 B (0.31730769 0.68269231)
## 92) texture_worst< 4.786713 78 32 B (0.41025641 0.58974359) *
## 93) texture_worst>=4.786713 26 1 B (0.03846154 0.96153846) *
## 47) symmetry_worst< -1.864441 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.504307 519 207 B (0.39884393 0.60115607)
## 6) smoothness_worst< -1.533657 365 172 B (0.47123288 0.52876712)
## 12) smoothness_worst>=-1.556752 113 42 M (0.62831858 0.37168142)
## 24) compactness_se< -4.087687 51 8 M (0.84313725 0.15686275)
## 48) smoothness_mean>=-2.469882 46 3 M (0.93478261 0.06521739)
## 96) smoothness_worst< -1.539367 43 0 M (1.00000000 0.00000000) *
## 97) smoothness_worst>=-1.539367 3 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean< -2.469882 5 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-4.087687 62 28 B (0.45161290 0.54838710)
## 50) compactness_se>=-3.569872 30 4 M (0.86666667 0.13333333)
## 100) symmetry_worst< -1.710027 23 0 M (1.00000000 0.00000000) *
## 101) symmetry_worst>=-1.710027 7 3 B (0.42857143 0.57142857) *
## 51) compactness_se< -3.569872 32 2 B (0.06250000 0.93750000)
## 102) symmetry_worst>=-1.617937 3 1 M (0.66666667 0.33333333) *
## 103) symmetry_worst< -1.617937 29 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.556752 252 101 B (0.40079365 0.59920635)
## 26) smoothness_worst< -1.563512 219 100 B (0.45662100 0.54337900)
## 52) texture_worst< 3.981173 11 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=3.981173 208 89 B (0.42788462 0.57211538)
## 106) texture_worst>=4.609399 111 49 M (0.55855856 0.44144144) *
## 107) texture_worst< 4.609399 97 27 B (0.27835052 0.72164948) *
## 27) smoothness_worst>=-1.563512 33 1 B (0.03030303 0.96969697)
## 54) texture_mean>=3.274729 1 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 3.274729 32 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.533657 154 35 B (0.22727273 0.77272727)
## 14) smoothness_mean< -2.329341 69 31 B (0.44927536 0.55072464)
## 28) smoothness_mean>=-2.369786 31 8 M (0.74193548 0.25806452)
## 56) compactness_se>=-3.979062 27 4 M (0.85185185 0.14814815)
## 112) compactness_se< -3.468609 23 0 M (1.00000000 0.00000000) *
## 113) compactness_se>=-3.468609 4 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -3.979062 4 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean< -2.369786 38 8 B (0.21052632 0.78947368)
## 58) symmetry_worst>=-1.567876 8 3 M (0.62500000 0.37500000)
## 116) smoothness_worst< -1.513087 5 0 M (1.00000000 0.00000000) *
## 117) smoothness_worst>=-1.513087 3 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.567876 30 3 B (0.10000000 0.90000000)
## 118) smoothness_mean< -2.438762 9 3 B (0.33333333 0.66666667) *
## 119) smoothness_mean>=-2.438762 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.329341 85 4 B (0.04705882 0.95294118)
## 30) texture_mean>=3.019196 26 4 B (0.15384615 0.84615385)
## 60) smoothness_mean>=-2.257258 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.257258 23 1 B (0.04347826 0.95652174)
## 122) texture_mean< 3.028188 1 0 M (1.00000000 0.00000000) *
## 123) texture_mean>=3.028188 22 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.019196 59 0 B (0.00000000 1.00000000) *
##
## $trees[[57]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.4912281 0.5087719)
## 2) smoothness_mean>=-2.546123 876 432 M (0.5068493 0.4931507)
## 4) compactness_se>=-4.704842 862 418 M (0.5150812 0.4849188)
## 8) compactness_se< -4.098353 230 81 M (0.6478261 0.3521739)
## 16) smoothness_mean< -2.289681 203 58 M (0.7142857 0.2857143)
## 32) texture_worst>=4.362076 183 42 M (0.7704918 0.2295082)
## 64) symmetry_worst< -1.484766 169 31 M (0.8165680 0.1834320) *
## 65) symmetry_worst>=-1.484766 14 3 B (0.2142857 0.7857143) *
## 33) texture_worst< 4.362076 20 4 B (0.2000000 0.8000000)
## 66) compactness_se>=-4.193609 6 2 M (0.6666667 0.3333333) *
## 67) compactness_se< -4.193609 14 0 B (0.0000000 1.0000000) *
## 17) smoothness_mean>=-2.289681 27 4 B (0.1481481 0.8518519)
## 34) smoothness_mean>=-2.222419 7 3 M (0.5714286 0.4285714)
## 68) texture_mean>=2.892399 4 0 M (1.0000000 0.0000000) *
## 69) texture_mean< 2.892399 3 0 B (0.0000000 1.0000000) *
## 35) smoothness_mean< -2.222419 20 0 B (0.0000000 1.0000000) *
## 9) compactness_se>=-4.098353 632 295 B (0.4667722 0.5332278)
## 18) compactness_se>=-4.05446 599 295 B (0.4924875 0.5075125)
## 36) smoothness_worst>=-1.499656 287 110 M (0.6167247 0.3832753)
## 72) smoothness_worst< -1.434076 203 63 M (0.6896552 0.3103448) *
## 73) smoothness_worst>=-1.434076 84 37 B (0.4404762 0.5595238) *
## 37) smoothness_worst< -1.499656 312 118 B (0.3782051 0.6217949)
## 74) texture_worst>=4.569119 187 90 B (0.4812834 0.5187166) *
## 75) texture_worst< 4.569119 125 28 B (0.2240000 0.7760000) *
## 19) compactness_se< -4.05446 33 0 B (0.0000000 1.0000000) *
## 5) compactness_se< -4.704842 14 0 B (0.0000000 1.0000000) *
## 3) smoothness_mean< -2.546123 36 4 B (0.1111111 0.8888889)
## 6) smoothness_worst< -1.720903 6 2 M (0.6666667 0.3333333)
## 12) texture_mean< 3.103494 4 0 M (1.0000000 0.0000000) *
## 13) texture_mean>=3.103494 2 0 B (0.0000000 1.0000000) *
## 7) smoothness_worst>=-1.720903 30 0 B (0.0000000 1.0000000) *
##
## $trees[[58]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 443 B (0.48574561 0.51425439)
## 2) symmetry_worst>=-1.068249 22 0 M (1.00000000 0.00000000) *
## 3) symmetry_worst< -1.068249 890 421 B (0.47303371 0.52696629)
## 6) texture_mean>=2.927988 554 257 M (0.53610108 0.46389892)
## 12) smoothness_worst>=-1.473476 121 33 M (0.72727273 0.27272727)
## 24) texture_worst< 4.76475 42 2 M (0.95238095 0.04761905)
## 48) texture_mean>=2.934384 41 1 M (0.97560976 0.02439024)
## 96) smoothness_mean< -2.107265 40 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.107265 1 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.934384 1 0 B (0.00000000 1.00000000) *
## 25) texture_worst>=4.76475 79 31 M (0.60759494 0.39240506)
## 50) texture_worst>=4.821213 69 21 M (0.69565217 0.30434783)
## 100) compactness_se< -3.379822 56 11 M (0.80357143 0.19642857) *
## 101) compactness_se>=-3.379822 13 3 B (0.23076923 0.76923077) *
## 51) texture_worst< 4.821213 10 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.473476 433 209 B (0.48267898 0.51732102)
## 26) smoothness_mean< -2.353249 289 125 M (0.56747405 0.43252595)
## 52) smoothness_mean>=-2.408446 98 23 M (0.76530612 0.23469388)
## 104) texture_worst< 4.876647 79 10 M (0.87341772 0.12658228) *
## 105) texture_worst>=4.876647 19 6 B (0.31578947 0.68421053) *
## 53) smoothness_mean< -2.408446 191 89 B (0.46596859 0.53403141)
## 106) texture_worst< 4.592462 53 9 M (0.83018868 0.16981132) *
## 107) texture_worst>=4.592462 138 45 B (0.32608696 0.67391304) *
## 27) smoothness_mean>=-2.353249 144 45 B (0.31250000 0.68750000)
## 54) smoothness_mean>=-2.303285 82 38 B (0.46341463 0.53658537)
## 108) compactness_se>=-3.470794 24 5 M (0.79166667 0.20833333) *
## 109) compactness_se< -3.470794 58 19 B (0.32758621 0.67241379) *
## 55) smoothness_mean< -2.303285 62 7 B (0.11290323 0.88709677)
## 110) texture_worst>=4.764475 14 7 M (0.50000000 0.50000000) *
## 111) texture_worst< 4.764475 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.927988 336 124 B (0.36904762 0.63095238)
## 14) symmetry_worst< -1.809006 150 70 M (0.53333333 0.46666667)
## 28) texture_worst< 4.400796 117 40 M (0.65811966 0.34188034)
## 56) smoothness_mean< -2.290163 98 21 M (0.78571429 0.21428571)
## 112) texture_mean< 2.903056 90 13 M (0.85555556 0.14444444) *
## 113) texture_mean>=2.903056 8 0 B (0.00000000 1.00000000) *
## 57) smoothness_mean>=-2.290163 19 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.400796 33 3 B (0.09090909 0.90909091)
## 58) compactness_se< -4.431402 8 3 B (0.37500000 0.62500000)
## 116) compactness_se>=-4.50262 3 0 M (1.00000000 0.00000000) *
## 117) compactness_se< -4.50262 5 0 B (0.00000000 1.00000000) *
## 59) compactness_se>=-4.431402 25 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst>=-1.809006 186 44 B (0.23655914 0.76344086)
## 30) smoothness_worst>=-1.520499 118 39 B (0.33050847 0.66949153)
## 60) smoothness_worst< -1.517609 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_worst>=-1.517609 110 31 B (0.28181818 0.71818182)
## 122) symmetry_worst>=-1.641484 76 30 B (0.39473684 0.60526316) *
## 123) symmetry_worst< -1.641484 34 1 B (0.02941176 0.97058824) *
## 31) smoothness_worst< -1.520499 68 5 B (0.07352941 0.92647059)
## 62) compactness_se< -4.159844 19 5 B (0.26315789 0.73684211)
## 124) compactness_se>=-4.166611 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -4.166611 15 1 B (0.06666667 0.93333333) *
## 63) compactness_se>=-4.159844 49 0 B (0.00000000 1.00000000) *
##
## $trees[[59]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 M (0.52850877 0.47149123)
## 2) symmetry_worst>=-1.785734 501 194 M (0.61277445 0.38722555)
## 4) texture_mean>=2.806989 443 152 M (0.65688488 0.34311512)
## 8) compactness_se>=-3.955455 259 58 M (0.77606178 0.22393822)
## 16) compactness_se< -2.86687 242 47 M (0.80578512 0.19421488)
## 32) symmetry_worst< -1.128751 230 39 M (0.83043478 0.16956522)
## 64) texture_mean< 3.36829 227 36 M (0.84140969 0.15859031) *
## 65) texture_mean>=3.36829 3 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst>=-1.128751 12 4 B (0.33333333 0.66666667)
## 66) smoothness_worst>=-1.49848 4 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst< -1.49848 8 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.86687 17 6 B (0.35294118 0.64705882)
## 34) texture_mean>=3.050024 5 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.050024 12 1 B (0.08333333 0.91666667)
## 70) smoothness_mean>=-2.161865 1 0 M (1.00000000 0.00000000) *
## 71) smoothness_mean< -2.161865 11 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.955455 184 90 B (0.48913043 0.51086957)
## 18) symmetry_worst< -1.733593 18 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst>=-1.733593 166 72 B (0.43373494 0.56626506)
## 38) smoothness_worst< -1.607486 30 4 M (0.86666667 0.13333333)
## 76) texture_mean< 3.296262 26 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=3.296262 4 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.607486 136 46 B (0.33823529 0.66176471)
## 78) symmetry_worst>=-1.577444 81 39 B (0.48148148 0.51851852) *
## 79) symmetry_worst< -1.577444 55 7 B (0.12727273 0.87272727) *
## 5) texture_mean< 2.806989 58 16 B (0.27586207 0.72413793)
## 10) smoothness_mean>=-2.232593 15 3 M (0.80000000 0.20000000)
## 20) symmetry_worst< -1.492909 10 0 M (1.00000000 0.00000000) *
## 21) symmetry_worst>=-1.492909 5 2 B (0.40000000 0.60000000)
## 42) smoothness_mean< -2.229802 2 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.229802 3 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.232593 43 4 B (0.09302326 0.90697674)
## 22) compactness_se>=-3.433945 8 4 M (0.50000000 0.50000000)
## 44) smoothness_worst>=-1.49622 4 0 M (1.00000000 0.00000000) *
## 45) smoothness_worst< -1.49622 4 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.433945 35 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.785734 411 175 B (0.42579075 0.57420925)
## 6) smoothness_worst>=-1.603315 345 164 B (0.47536232 0.52463768)
## 12) smoothness_worst< -1.59596 28 1 M (0.96428571 0.03571429)
## 24) texture_mean>=2.85796 18 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.85796 10 1 M (0.90000000 0.10000000)
## 50) texture_mean< 2.793316 9 0 M (1.00000000 0.00000000) *
## 51) texture_mean>=2.793316 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.59596 317 137 B (0.43217666 0.56782334)
## 26) texture_mean>=3.043808 126 47 M (0.62698413 0.37301587)
## 52) texture_mean< 3.21023 72 8 M (0.88888889 0.11111111)
## 104) smoothness_worst>=-1.583806 68 4 M (0.94117647 0.05882353) *
## 105) smoothness_worst< -1.583806 4 0 B (0.00000000 1.00000000) *
## 53) texture_mean>=3.21023 54 15 B (0.27777778 0.72222222)
## 106) texture_worst>=5.073596 29 14 M (0.51724138 0.48275862) *
## 107) texture_worst< 5.073596 25 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 3.043808 191 58 B (0.30366492 0.69633508)
## 54) smoothness_worst>=-1.500665 78 35 B (0.44871795 0.55128205)
## 108) smoothness_worst< -1.476411 35 10 M (0.71428571 0.28571429) *
## 109) smoothness_worst>=-1.476411 43 10 B (0.23255814 0.76744186) *
## 55) smoothness_worst< -1.500665 113 23 B (0.20353982 0.79646018)
## 110) smoothness_worst< -1.594361 5 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.594361 108 18 B (0.16666667 0.83333333) *
## 7) smoothness_worst< -1.603315 66 11 B (0.16666667 0.83333333)
## 14) compactness_se< -4.6643 15 6 M (0.60000000 0.40000000)
## 28) smoothness_mean< -2.535018 9 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.535018 6 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.6643 51 2 B (0.03921569 0.96078431)
## 30) smoothness_worst< -1.720903 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=3.026052 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 3.026052 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.720903 48 0 B (0.00000000 1.00000000) *
##
## $trees[[60]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 453 B (0.49671053 0.50328947)
## 2) symmetry_worst>=-1.424186 86 25 M (0.70930233 0.29069767)
## 4) texture_worst>=4.647183 42 4 M (0.90476190 0.09523810)
## 8) smoothness_worst>=-1.49649 36 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.49649 6 2 B (0.33333333 0.66666667)
## 18) smoothness_mean< -2.311841 2 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.311841 4 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.647183 44 21 M (0.52272727 0.47727273)
## 10) smoothness_mean>=-2.217831 15 2 M (0.86666667 0.13333333)
## 20) smoothness_mean< -2.022167 13 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean>=-2.022167 2 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.217831 29 10 B (0.34482759 0.65517241)
## 22) texture_worst< 4.136225 10 0 M (1.00000000 0.00000000) *
## 23) texture_worst>=4.136225 19 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.424186 826 392 B (0.47457627 0.52542373)
## 6) texture_worst>=4.260219 718 356 M (0.50417827 0.49582173)
## 12) compactness_se< -4.116284 223 83 M (0.62780269 0.37219731)
## 24) symmetry_worst< -1.508268 209 70 M (0.66507177 0.33492823)
## 48) compactness_se>=-4.705732 200 61 M (0.69500000 0.30500000)
## 96) texture_worst>=4.339889 194 55 M (0.71649485 0.28350515) *
## 97) texture_worst< 4.339889 6 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.705732 9 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.508268 14 1 B (0.07142857 0.92857143)
## 50) compactness_se>=-4.234991 1 0 M (1.00000000 0.00000000) *
## 51) compactness_se< -4.234991 13 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-4.116284 495 222 B (0.44848485 0.55151515)
## 26) texture_mean< 2.747587 23 2 M (0.91304348 0.08695652)
## 52) texture_mean>=2.697516 21 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.697516 2 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.747587 472 201 B (0.42584746 0.57415254)
## 54) texture_mean>=2.927988 378 182 B (0.48148148 0.51851852)
## 108) texture_mean< 2.940483 29 1 M (0.96551724 0.03448276) *
## 109) texture_mean>=2.940483 349 154 B (0.44126074 0.55873926) *
## 55) texture_mean< 2.927988 94 19 B (0.20212766 0.79787234)
## 110) smoothness_worst>=-1.45348 13 4 M (0.69230769 0.30769231) *
## 111) smoothness_worst< -1.45348 81 10 B (0.12345679 0.87654321) *
## 7) texture_worst< 4.260219 108 30 B (0.27777778 0.72222222)
## 14) compactness_se>=-3.894783 70 30 B (0.42857143 0.57142857)
## 28) compactness_se< -3.48221 51 21 M (0.58823529 0.41176471)
## 56) texture_worst< 4.206328 37 7 M (0.81081081 0.18918919)
## 112) compactness_se>=-3.764682 22 0 M (1.00000000 0.00000000) *
## 113) compactness_se< -3.764682 15 7 M (0.53333333 0.46666667) *
## 57) texture_worst>=4.206328 14 0 B (0.00000000 1.00000000) *
## 29) compactness_se>=-3.48221 19 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.894783 38 0 B (0.00000000 1.00000000) *
##
## $trees[[61]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 392 M (0.57017544 0.42982456)
## 2) smoothness_worst>=-1.525694 520 182 M (0.65000000 0.35000000)
## 4) texture_mean>=3.06081 166 31 M (0.81325301 0.18674699)
## 8) smoothness_mean< -2.301586 103 10 M (0.90291262 0.09708738)
## 16) compactness_se< -3.106177 97 4 M (0.95876289 0.04123711)
## 32) compactness_se>=-4.507761 95 2 M (0.97894737 0.02105263)
## 64) texture_mean< 3.355261 86 0 M (1.00000000 0.00000000) *
## 65) texture_mean>=3.355261 9 2 M (0.77777778 0.22222222) *
## 33) compactness_se< -4.507761 2 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.106177 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.301586 63 21 M (0.66666667 0.33333333)
## 18) smoothness_mean>=-2.257137 28 3 M (0.89285714 0.10714286)
## 36) smoothness_mean< -2.105484 25 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean>=-2.105484 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.257137 35 17 B (0.48571429 0.51428571)
## 38) texture_worst>=4.965981 9 0 M (1.00000000 0.00000000) *
## 39) texture_worst< 4.965981 26 8 B (0.30769231 0.69230769)
## 78) compactness_se>=-3.444069 6 0 M (1.00000000 0.00000000) *
## 79) compactness_se< -3.444069 20 2 B (0.10000000 0.90000000) *
## 5) texture_mean< 3.06081 354 151 M (0.57344633 0.42655367)
## 10) smoothness_worst>=-1.496637 287 106 M (0.63066202 0.36933798)
## 20) texture_mean< 2.987952 209 60 M (0.71291866 0.28708134)
## 40) symmetry_worst>=-1.864441 166 34 M (0.79518072 0.20481928)
## 80) texture_worst>=4.194566 145 21 M (0.85517241 0.14482759) *
## 81) texture_worst< 4.194566 21 8 B (0.38095238 0.61904762) *
## 41) symmetry_worst< -1.864441 43 17 B (0.39534884 0.60465116)
## 82) smoothness_worst< -1.480334 18 3 M (0.83333333 0.16666667) *
## 83) smoothness_worst>=-1.480334 25 2 B (0.08000000 0.92000000) *
## 21) texture_mean>=2.987952 78 32 B (0.41025641 0.58974359)
## 42) texture_worst>=4.874946 19 4 M (0.78947368 0.21052632)
## 84) compactness_se>=-4.030876 14 0 M (1.00000000 0.00000000) *
## 85) compactness_se< -4.030876 5 1 B (0.20000000 0.80000000) *
## 43) texture_worst< 4.874946 59 17 B (0.28813559 0.71186441)
## 86) compactness_se< -4.280193 7 1 M (0.85714286 0.14285714) *
## 87) compactness_se>=-4.280193 52 11 B (0.21153846 0.78846154) *
## 11) smoothness_worst< -1.496637 67 22 B (0.32835821 0.67164179)
## 22) symmetry_worst< -1.736492 39 18 M (0.53846154 0.46153846)
## 44) compactness_se< -3.210824 27 6 M (0.77777778 0.22222222)
## 88) compactness_se>=-3.979062 21 1 M (0.95238095 0.04761905) *
## 89) compactness_se< -3.979062 6 1 B (0.16666667 0.83333333) *
## 45) compactness_se>=-3.210824 12 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.736492 28 1 B (0.03571429 0.96428571)
## 46) texture_mean>=3.01402 1 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.01402 27 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.525694 392 182 B (0.46428571 0.53571429)
## 6) compactness_se>=-3.610301 135 51 M (0.62222222 0.37777778)
## 12) smoothness_mean>=-2.396197 68 14 M (0.79411765 0.20588235)
## 24) smoothness_worst< -1.527573 64 10 M (0.84375000 0.15625000)
## 48) texture_worst>=4.411908 54 4 M (0.92592593 0.07407407)
## 96) smoothness_worst>=-1.618016 49 1 M (0.97959184 0.02040816) *
## 97) smoothness_worst< -1.618016 5 2 B (0.40000000 0.60000000) *
## 49) texture_worst< 4.411908 10 4 B (0.40000000 0.60000000)
## 98) compactness_se< -3.492332 4 0 M (1.00000000 0.00000000) *
## 99) compactness_se>=-3.492332 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.527573 4 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.396197 67 30 B (0.44776119 0.55223881)
## 26) compactness_se< -3.593774 15 0 M (1.00000000 0.00000000) *
## 27) compactness_se>=-3.593774 52 15 B (0.28846154 0.71153846)
## 54) smoothness_worst>=-1.604936 29 14 B (0.48275862 0.51724138)
## 108) smoothness_worst< -1.594363 12 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst>=-1.594363 17 2 B (0.11764706 0.88235294) *
## 55) smoothness_worst< -1.604936 23 1 B (0.04347826 0.95652174)
## 110) smoothness_worst< -1.720903 1 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.720903 22 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.610301 257 98 B (0.38132296 0.61867704)
## 14) compactness_se< -4.579712 47 14 M (0.70212766 0.29787234)
## 28) compactness_se>=-4.711555 36 4 M (0.88888889 0.11111111)
## 56) smoothness_worst< -1.549205 33 1 M (0.96969697 0.03030303)
## 112) smoothness_worst>=-1.609211 25 0 M (1.00000000 0.00000000) *
## 113) smoothness_worst< -1.609211 8 1 M (0.87500000 0.12500000) *
## 57) smoothness_worst>=-1.549205 3 0 B (0.00000000 1.00000000) *
## 29) compactness_se< -4.711555 11 1 B (0.09090909 0.90909091)
## 58) symmetry_worst>=-1.179946 1 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.179946 10 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.579712 210 65 B (0.30952381 0.69047619)
## 30) smoothness_mean< -2.382983 153 61 B (0.39869281 0.60130719)
## 60) smoothness_mean>=-2.434747 69 25 M (0.63768116 0.36231884)
## 120) smoothness_worst< -1.538735 59 15 M (0.74576271 0.25423729) *
## 121) smoothness_worst>=-1.538735 10 0 B (0.00000000 1.00000000) *
## 61) smoothness_mean< -2.434747 84 17 B (0.20238095 0.79761905)
## 122) texture_worst< 4.447343 21 10 M (0.52380952 0.47619048) *
## 123) texture_worst>=4.447343 63 6 B (0.09523810 0.90476190) *
## 31) smoothness_mean>=-2.382983 57 4 B (0.07017544 0.92982456)
## 62) compactness_se>=-3.721403 10 4 B (0.40000000 0.60000000)
## 124) compactness_se< -3.62066 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.62066 6 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.721403 47 0 B (0.00000000 1.00000000) *
##
## $trees[[62]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 437 M (0.52083333 0.47916667)
## 2) texture_mean>=2.963467 519 198 M (0.61849711 0.38150289)
## 4) symmetry_worst>=-2.01934 408 134 M (0.67156863 0.32843137)
## 8) smoothness_worst>=-1.660611 399 125 M (0.68671679 0.31328321)
## 16) texture_worst>=5.06141 82 12 M (0.85365854 0.14634146)
## 32) symmetry_worst< -1.450078 73 6 M (0.91780822 0.08219178)
## 64) texture_worst< 5.386175 56 0 M (1.00000000 0.00000000) *
## 65) texture_worst>=5.386175 17 6 M (0.64705882 0.35294118) *
## 33) symmetry_worst>=-1.450078 9 3 B (0.33333333 0.66666667)
## 66) texture_mean< 3.217018 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.217018 6 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 5.06141 317 113 M (0.64353312 0.35646688)
## 34) smoothness_mean>=-2.42138 234 68 M (0.70940171 0.29059829)
## 68) smoothness_mean< -2.352368 64 7 M (0.89062500 0.10937500) *
## 69) smoothness_mean>=-2.352368 170 61 M (0.64117647 0.35882353) *
## 35) smoothness_mean< -2.42138 83 38 B (0.45783133 0.54216867)
## 70) compactness_se< -4.014684 46 13 M (0.71739130 0.28260870) *
## 71) compactness_se>=-4.014684 37 5 B (0.13513514 0.86486486) *
## 9) smoothness_worst< -1.660611 9 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -2.01934 111 47 B (0.42342342 0.57657658)
## 10) compactness_se>=-3.551587 48 15 M (0.68750000 0.31250000)
## 20) smoothness_worst>=-1.601489 36 6 M (0.83333333 0.16666667)
## 40) texture_worst< 5.216315 32 2 M (0.93750000 0.06250000)
## 80) texture_mean>=3.049609 30 0 M (1.00000000 0.00000000) *
## 81) texture_mean< 3.049609 2 0 B (0.00000000 1.00000000) *
## 41) texture_worst>=5.216315 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.601489 12 3 B (0.25000000 0.75000000)
## 42) texture_worst< 4.59024 4 1 M (0.75000000 0.25000000)
## 84) texture_mean>=3.032942 3 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 3.032942 1 0 B (0.00000000 1.00000000) *
## 43) texture_worst>=4.59024 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.551587 63 14 B (0.22222222 0.77777778)
## 22) texture_mean< 3.038878 8 1 M (0.87500000 0.12500000)
## 44) texture_mean>=3.000441 7 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.000441 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=3.038878 55 7 B (0.12727273 0.87272727)
## 46) smoothness_mean>=-2.290398 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.290398 54 6 B (0.11111111 0.88888889)
## 94) symmetry_worst>=-2.052205 17 5 B (0.29411765 0.70588235) *
## 95) symmetry_worst< -2.052205 37 1 B (0.02702703 0.97297297) *
## 3) texture_mean< 2.963467 393 154 B (0.39185751 0.60814249)
## 6) smoothness_worst>=-1.451541 71 15 M (0.78873239 0.21126761)
## 12) smoothness_mean< -2.240129 38 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean>=-2.240129 33 15 M (0.54545455 0.45454545)
## 26) texture_mean< 2.757784 17 3 M (0.82352941 0.17647059)
## 52) smoothness_mean< -1.889548 16 2 M (0.87500000 0.12500000)
## 104) compactness_se>=-3.896708 15 1 M (0.93333333 0.06666667) *
## 105) compactness_se< -3.896708 1 0 B (0.00000000 1.00000000) *
## 53) smoothness_mean>=-1.889548 1 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.757784 16 4 B (0.25000000 0.75000000)
## 54) texture_mean>=2.935178 3 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 2.935178 13 1 B (0.07692308 0.92307692)
## 110) smoothness_worst< -1.437625 1 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.437625 12 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.451541 322 98 B (0.30434783 0.69565217)
## 14) symmetry_worst>=-1.327359 23 6 M (0.73913043 0.26086957)
## 28) symmetry_worst< -1.23578 13 0 M (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.23578 10 4 B (0.40000000 0.60000000)
## 58) smoothness_worst< -1.461111 4 0 M (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.461111 6 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.327359 299 81 B (0.27090301 0.72909699)
## 30) smoothness_mean< -2.441446 77 38 M (0.50649351 0.49350649)
## 60) symmetry_worst>=-1.816978 43 9 M (0.79069767 0.20930233)
## 120) texture_worst< 4.644924 38 4 M (0.89473684 0.10526316) *
## 121) texture_worst>=4.644924 5 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst< -1.816978 34 5 B (0.14705882 0.85294118)
## 122) smoothness_mean>=-2.443746 5 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.443746 29 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.441446 222 42 B (0.18918919 0.81081081)
## 62) compactness_se< -4.116284 58 20 B (0.34482759 0.65517241)
## 124) compactness_se>=-4.201715 17 2 M (0.88235294 0.11764706) *
## 125) compactness_se< -4.201715 41 5 B (0.12195122 0.87804878) *
## 63) compactness_se>=-4.116284 164 22 B (0.13414634 0.86585366)
## 126) smoothness_worst>=-1.472307 12 5 B (0.41666667 0.58333333) *
## 127) smoothness_worst< -1.472307 152 17 B (0.11184211 0.88815789) *
##
## $trees[[63]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_mean>=3.065024 297 112 M (0.62289562 0.37710438)
## 4) smoothness_worst>=-1.618721 268 86 M (0.67910448 0.32089552)
## 8) texture_worst< 4.781945 55 1 M (0.98181818 0.01818182)
## 16) texture_worst>=4.52814 54 0 M (1.00000000 0.00000000) *
## 17) texture_worst< 4.52814 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.781945 213 85 M (0.60093897 0.39906103)
## 18) texture_worst>=4.820212 185 63 M (0.65945946 0.34054054)
## 36) symmetry_worst>=-1.71268 74 13 M (0.82432432 0.17567568)
## 72) smoothness_mean>=-2.509617 71 10 M (0.85915493 0.14084507) *
## 73) smoothness_mean< -2.509617 3 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.71268 111 50 M (0.54954955 0.45045045)
## 74) symmetry_worst< -1.733593 91 30 M (0.67032967 0.32967033) *
## 75) symmetry_worst>=-1.733593 20 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 4.820212 28 6 B (0.21428571 0.78571429)
## 38) compactness_se>=-3.052779 3 0 M (1.00000000 0.00000000) *
## 39) compactness_se< -3.052779 25 3 B (0.12000000 0.88000000)
## 78) smoothness_worst>=-1.444063 3 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.444063 22 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.618721 29 3 B (0.10344828 0.89655172)
## 10) texture_worst< 4.609308 4 1 M (0.75000000 0.25000000)
## 20) texture_mean>=3.075433 3 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.075433 1 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.609308 25 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.065024 615 285 B (0.46341463 0.53658537)
## 6) smoothness_worst>=-1.472307 174 62 M (0.64367816 0.35632184)
## 12) compactness_se>=-4.032549 123 32 M (0.73983740 0.26016260)
## 24) compactness_se< -3.931945 30 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-3.931945 93 32 M (0.65591398 0.34408602)
## 50) symmetry_worst< -1.486964 68 16 M (0.76470588 0.23529412)
## 100) symmetry_worst>=-1.828219 53 5 M (0.90566038 0.09433962) *
## 101) symmetry_worst< -1.828219 15 4 B (0.26666667 0.73333333) *
## 51) symmetry_worst>=-1.486964 25 9 B (0.36000000 0.64000000)
## 102) smoothness_mean>=-2.287745 16 7 M (0.56250000 0.43750000) *
## 103) smoothness_mean< -2.287745 9 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.032549 51 21 B (0.41176471 0.58823529)
## 26) smoothness_worst< -1.458214 22 2 M (0.90909091 0.09090909)
## 52) texture_mean>=2.901883 20 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.901883 2 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.458214 29 1 B (0.03448276 0.96551724)
## 54) symmetry_worst< -1.741496 4 1 B (0.25000000 0.75000000)
## 108) symmetry_worst>=-1.780237 1 0 M (1.00000000 0.00000000) *
## 109) symmetry_worst< -1.780237 3 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst>=-1.741496 25 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.472307 441 173 B (0.39229025 0.60770975)
## 14) smoothness_worst< -1.476997 400 171 B (0.42750000 0.57250000)
## 28) smoothness_worst>=-1.482701 34 3 M (0.91176471 0.08823529)
## 56) compactness_se>=-4.290267 32 1 M (0.96875000 0.03125000)
## 112) texture_mean>=2.732378 31 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.732378 1 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -4.290267 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.482701 366 140 B (0.38251366 0.61748634)
## 58) compactness_se< -3.476676 299 133 B (0.44481605 0.55518395)
## 116) compactness_se>=-3.716111 71 9 M (0.87323944 0.12676056) *
## 117) compactness_se< -3.716111 228 71 B (0.31140351 0.68859649) *
## 59) compactness_se>=-3.476676 67 7 B (0.10447761 0.89552239)
## 118) symmetry_worst>=-1.474719 7 1 M (0.85714286 0.14285714) *
## 119) symmetry_worst< -1.474719 60 1 B (0.01666667 0.98333333) *
## 15) smoothness_worst>=-1.476997 41 2 B (0.04878049 0.95121951)
## 30) texture_worst>=4.844547 2 0 M (1.00000000 0.00000000) *
## 31) texture_worst< 4.844547 39 0 B (0.00000000 1.00000000) *
##
## $trees[[64]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) compactness_se< -4.49319 106 31 M (0.70754717 0.29245283)
## 4) compactness_se>=-4.704842 95 20 M (0.78947368 0.21052632)
## 8) symmetry_worst< -1.509002 87 12 M (0.86206897 0.13793103)
## 16) texture_mean< 3.232565 84 9 M (0.89285714 0.10714286)
## 32) texture_mean>=2.846651 82 7 M (0.91463415 0.08536585)
## 64) smoothness_mean< -2.295268 80 5 M (0.93750000 0.06250000) *
## 65) smoothness_mean>=-2.295268 2 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.846651 2 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.232565 3 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.509002 8 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -4.704842 11 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-4.49319 806 358 B (0.44416873 0.55583127)
## 6) smoothness_worst>=-1.559144 598 295 B (0.49331104 0.50668896)
## 12) symmetry_worst< -1.781339 272 109 M (0.59926471 0.40073529)
## 24) smoothness_mean< -2.313857 158 42 M (0.73417722 0.26582278)
## 48) compactness_se< -3.455891 144 29 M (0.79861111 0.20138889)
## 96) symmetry_worst>=-2.233349 134 19 M (0.85820896 0.14179104) *
## 97) symmetry_worst< -2.233349 10 0 B (0.00000000 1.00000000) *
## 49) compactness_se>=-3.455891 14 1 B (0.07142857 0.92857143)
## 98) smoothness_mean< -2.465359 1 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.465359 13 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.313857 114 47 B (0.41228070 0.58771930)
## 50) smoothness_worst>=-1.499656 76 33 M (0.56578947 0.43421053)
## 100) smoothness_mean< -2.219625 49 13 M (0.73469388 0.26530612) *
## 101) smoothness_mean>=-2.219625 27 7 B (0.25925926 0.74074074) *
## 51) smoothness_worst< -1.499656 38 4 B (0.10526316 0.89473684)
## 102) compactness_se>=-3.239565 6 3 M (0.50000000 0.50000000) *
## 103) compactness_se< -3.239565 32 1 B (0.03125000 0.96875000) *
## 13) symmetry_worst>=-1.781339 326 132 B (0.40490798 0.59509202)
## 26) symmetry_worst>=-1.524537 86 36 M (0.58139535 0.41860465)
## 52) texture_mean>=2.777879 68 20 M (0.70588235 0.29411765)
## 104) symmetry_worst< -1.124686 55 10 M (0.81818182 0.18181818) *
## 105) symmetry_worst>=-1.124686 13 3 B (0.23076923 0.76923077) *
## 53) texture_mean< 2.777879 18 2 B (0.11111111 0.88888889)
## 106) compactness_se>=-3.173162 2 0 M (1.00000000 0.00000000) *
## 107) compactness_se< -3.173162 16 0 B (0.00000000 1.00000000) *
## 27) symmetry_worst< -1.524537 240 82 B (0.34166667 0.65833333)
## 54) smoothness_mean>=-2.413908 195 78 B (0.40000000 0.60000000)
## 108) smoothness_worst< -1.531349 15 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst>=-1.531349 180 63 B (0.35000000 0.65000000) *
## 55) smoothness_mean< -2.413908 45 4 B (0.08888889 0.91111111)
## 110) texture_worst>=5.003123 4 0 M (1.00000000 0.00000000) *
## 111) texture_worst< 5.003123 41 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.559144 208 63 B (0.30288462 0.69711538)
## 14) smoothness_mean>=-2.302636 14 2 M (0.85714286 0.14285714)
## 28) compactness_se>=-3.929833 12 0 M (1.00000000 0.00000000) *
## 29) compactness_se< -3.929833 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.302636 194 51 B (0.26288660 0.73711340)
## 30) symmetry_worst>=-1.538661 25 8 M (0.68000000 0.32000000)
## 60) texture_mean>=2.989073 17 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.989073 8 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.538661 169 34 B (0.20118343 0.79881657)
## 62) compactness_se>=-3.489046 38 16 B (0.42105263 0.57894737)
## 124) texture_mean>=3.136493 8 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.136493 30 8 B (0.26666667 0.73333333) *
## 63) compactness_se< -3.489046 131 18 B (0.13740458 0.86259542)
## 126) texture_worst< 4.679785 66 18 B (0.27272727 0.72727273) *
## 127) texture_worst>=4.679785 65 0 B (0.00000000 1.00000000) *
##
## $trees[[65]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 M (0.52302632 0.47697368)
## 2) texture_worst>=4.260219 805 357 M (0.55652174 0.44347826)
## 4) smoothness_worst>=-1.424105 61 9 M (0.85245902 0.14754098)
## 8) smoothness_mean>=-2.361754 57 5 M (0.91228070 0.08771930)
## 16) compactness_se>=-4.130421 56 4 M (0.92857143 0.07142857)
## 32) smoothness_mean< -2.093138 48 1 M (0.97916667 0.02083333)
## 64) smoothness_mean< -2.170242 39 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.170242 9 1 M (0.88888889 0.11111111) *
## 33) smoothness_mean>=-2.093138 8 3 M (0.62500000 0.37500000)
## 66) texture_mean< 2.970462 5 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=2.970462 3 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.130421 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.361754 4 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.424105 744 348 M (0.53225806 0.46774194)
## 10) smoothness_mean< -2.261445 642 281 M (0.56230530 0.43769470)
## 20) symmetry_worst>=-2.01934 506 196 M (0.61264822 0.38735178)
## 40) smoothness_worst>=-1.52112 258 74 M (0.71317829 0.28682171)
## 80) texture_worst< 4.545891 84 10 M (0.88095238 0.11904762) *
## 81) texture_worst>=4.545891 174 64 M (0.63218391 0.36781609) *
## 41) smoothness_worst< -1.52112 248 122 M (0.50806452 0.49193548)
## 82) symmetry_worst>=-1.549706 56 16 M (0.71428571 0.28571429) *
## 83) symmetry_worst< -1.549706 192 86 B (0.44791667 0.55208333) *
## 21) symmetry_worst< -2.01934 136 51 B (0.37500000 0.62500000)
## 42) symmetry_worst< -2.49184 17 1 M (0.94117647 0.05882353)
## 84) texture_mean< 3.310501 16 0 M (1.00000000 0.00000000) *
## 85) texture_mean>=3.310501 1 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst>=-2.49184 119 35 B (0.29411765 0.70588235)
## 86) smoothness_mean< -2.352958 83 33 B (0.39759036 0.60240964) *
## 87) smoothness_mean>=-2.352958 36 2 B (0.05555556 0.94444444) *
## 11) smoothness_mean>=-2.261445 102 35 B (0.34313725 0.65686275)
## 22) smoothness_mean>=-2.201842 16 2 M (0.87500000 0.12500000)
## 44) texture_worst>=4.450297 14 0 M (1.00000000 0.00000000) *
## 45) texture_worst< 4.450297 2 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.201842 86 21 B (0.24418605 0.75581395)
## 46) texture_mean>=3.050442 13 4 M (0.69230769 0.30769231)
## 92) compactness_se>=-4.008292 9 0 M (1.00000000 0.00000000) *
## 93) compactness_se< -4.008292 4 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 3.050442 73 12 B (0.16438356 0.83561644)
## 94) symmetry_worst>=-1.802807 41 12 B (0.29268293 0.70731707) *
## 95) symmetry_worst< -1.802807 32 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.260219 107 29 B (0.27102804 0.72897196)
## 6) symmetry_worst>=-1.567877 22 7 M (0.68181818 0.31818182)
## 12) texture_mean>=2.756192 10 0 M (1.00000000 0.00000000) *
## 13) texture_mean< 2.756192 12 5 B (0.41666667 0.58333333)
## 26) texture_mean< 2.518783 4 0 M (1.00000000 0.00000000) *
## 27) texture_mean>=2.518783 8 1 B (0.12500000 0.87500000)
## 54) compactness_se>=-3.3026 1 0 M (1.00000000 0.00000000) *
## 55) compactness_se< -3.3026 7 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.567877 85 14 B (0.16470588 0.83529412)
## 14) smoothness_worst< -1.54469 39 12 B (0.30769231 0.69230769)
## 28) smoothness_worst>=-1.545117 6 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.545117 33 6 B (0.18181818 0.81818182)
## 58) texture_mean>=2.764104 12 6 M (0.50000000 0.50000000)
## 116) compactness_se>=-3.607729 6 0 M (1.00000000 0.00000000) *
## 117) compactness_se< -3.607729 6 0 B (0.00000000 1.00000000) *
## 59) texture_mean< 2.764104 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.54469 46 2 B (0.04347826 0.95652174)
## 30) compactness_se< -3.823116 17 2 B (0.11764706 0.88235294)
## 60) compactness_se>=-3.894783 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -3.894783 15 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-3.823116 29 0 B (0.00000000 1.00000000) *
##
## $trees[[66]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 M (0.53399123 0.46600877)
## 2) texture_mean>=2.824054 799 353 M (0.55819775 0.44180225)
## 4) texture_worst< 4.644679 402 144 M (0.64179104 0.35820896)
## 8) smoothness_worst>=-1.451542 57 2 M (0.96491228 0.03508772)
## 16) texture_worst>=4.226553 56 1 M (0.98214286 0.01785714)
## 32) smoothness_worst< -1.349735 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst>=-1.349735 2 1 M (0.50000000 0.50000000)
## 66) texture_mean>=2.957438 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.957438 1 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.226553 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.451542 345 142 M (0.58840580 0.41159420)
## 18) smoothness_worst< -1.476997 312 111 M (0.64423077 0.35576923)
## 36) texture_worst>=4.614897 56 4 M (0.92857143 0.07142857)
## 72) compactness_se>=-4.694501 53 1 M (0.98113208 0.01886792) *
## 73) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 37) texture_worst< 4.614897 256 107 M (0.58203125 0.41796875)
## 74) symmetry_worst< -1.561818 223 80 M (0.64125561 0.35874439) *
## 75) symmetry_worst>=-1.561818 33 6 B (0.18181818 0.81818182) *
## 19) smoothness_worst>=-1.476997 33 2 B (0.06060606 0.93939394)
## 38) texture_mean>=2.932513 3 1 M (0.66666667 0.33333333)
## 76) texture_mean< 2.963141 2 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=2.963141 1 0 B (0.00000000 1.00000000) *
## 39) texture_mean< 2.932513 30 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.644679 397 188 B (0.47355164 0.52644836)
## 10) symmetry_worst>=-1.660659 184 71 M (0.61413043 0.38586957)
## 20) symmetry_worst< -1.608146 43 2 M (0.95348837 0.04651163)
## 40) texture_mean>=2.970637 42 1 M (0.97619048 0.02380952)
## 80) texture_mean< 3.129266 41 0 M (1.00000000 0.00000000) *
## 81) texture_mean>=3.129266 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.970637 1 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.608146 141 69 M (0.51063830 0.48936170)
## 42) smoothness_mean>=-2.281815 28 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.281815 113 44 B (0.38938053 0.61061947)
## 86) compactness_se< -4.081893 36 10 M (0.72222222 0.27777778) *
## 87) compactness_se>=-4.081893 77 18 B (0.23376623 0.76623377) *
## 11) symmetry_worst< -1.660659 213 75 B (0.35211268 0.64788732)
## 22) texture_worst>=4.837624 125 59 B (0.47200000 0.52800000)
## 44) texture_worst< 4.985267 25 2 M (0.92000000 0.08000000)
## 88) symmetry_worst>=-2.207844 24 1 M (0.95833333 0.04166667) *
## 89) symmetry_worst< -2.207844 1 0 B (0.00000000 1.00000000) *
## 45) texture_worst>=4.985267 100 36 B (0.36000000 0.64000000)
## 90) compactness_se>=-4.248059 63 30 B (0.47619048 0.52380952) *
## 91) compactness_se< -4.248059 37 6 B (0.16216216 0.83783784) *
## 23) texture_worst< 4.837624 88 16 B (0.18181818 0.81818182)
## 46) compactness_se>=-2.790746 6 0 M (1.00000000 0.00000000) *
## 47) compactness_se< -2.790746 82 10 B (0.12195122 0.87804878)
## 94) symmetry_worst< -2.121358 22 9 B (0.40909091 0.59090909) *
## 95) symmetry_worst>=-2.121358 60 1 B (0.01666667 0.98333333) *
## 3) texture_mean< 2.824054 113 41 B (0.36283186 0.63716814)
## 6) compactness_se>=-3.964431 83 41 B (0.49397590 0.50602410)
## 12) texture_worst< 4.328009 73 32 M (0.56164384 0.43835616)
## 24) texture_worst>=3.804403 63 23 M (0.63492063 0.36507937)
## 48) texture_mean< 2.771335 45 11 M (0.75555556 0.24444444)
## 96) compactness_se>=-3.891799 43 9 M (0.79069767 0.20930233) *
## 97) compactness_se< -3.891799 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean>=2.771335 18 6 B (0.33333333 0.66666667)
## 98) texture_mean>=2.811204 6 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.811204 12 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.804403 10 1 B (0.10000000 0.90000000)
## 50) smoothness_mean< -2.298096 2 1 M (0.50000000 0.50000000)
## 100) texture_mean< 2.673405 1 0 M (1.00000000 0.00000000) *
## 101) texture_mean>=2.673405 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean>=-2.298096 8 0 B (0.00000000 1.00000000) *
## 13) texture_worst>=4.328009 10 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.964431 30 0 B (0.00000000 1.00000000) *
##
## $trees[[67]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 M (0.50877193 0.49122807)
## 2) smoothness_mean>=-2.546123 895 431 M (0.51843575 0.48156425)
## 4) texture_worst>=4.572846 507 213 M (0.57988166 0.42011834)
## 8) smoothness_worst< -1.484675 342 116 M (0.66081871 0.33918129)
## 16) smoothness_mean>=-2.403622 165 34 M (0.79393939 0.20606061)
## 32) symmetry_worst>=-2.207988 153 22 M (0.85620915 0.14379085)
## 64) smoothness_mean< -2.286221 131 9 M (0.93129771 0.06870229) *
## 65) smoothness_mean>=-2.286221 22 9 B (0.40909091 0.59090909) *
## 33) symmetry_worst< -2.207988 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.403622 177 82 M (0.53672316 0.46327684)
## 34) smoothness_worst< -1.568573 83 21 M (0.74698795 0.25301205)
## 68) symmetry_worst>=-1.966444 71 11 M (0.84507042 0.15492958) *
## 69) symmetry_worst< -1.966444 12 2 B (0.16666667 0.83333333) *
## 35) smoothness_worst>=-1.568573 94 33 B (0.35106383 0.64893617)
## 70) texture_worst< 4.982438 56 27 M (0.51785714 0.48214286) *
## 71) texture_worst>=4.982438 38 4 B (0.10526316 0.89473684) *
## 9) smoothness_worst>=-1.484675 165 68 B (0.41212121 0.58787879)
## 18) smoothness_mean>=-2.284747 59 21 M (0.64406780 0.35593220)
## 36) compactness_se>=-4.032549 44 9 M (0.79545455 0.20454545)
## 72) smoothness_mean< -2.093138 37 2 M (0.94594595 0.05405405) *
## 73) smoothness_mean>=-2.093138 7 0 B (0.00000000 1.00000000) *
## 37) compactness_se< -4.032549 15 3 B (0.20000000 0.80000000)
## 74) texture_mean< 2.979048 5 2 M (0.60000000 0.40000000) *
## 75) texture_mean>=2.979048 10 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.284747 106 30 B (0.28301887 0.71698113)
## 38) texture_worst< 4.624204 5 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.624204 101 25 B (0.24752475 0.75247525)
## 78) symmetry_worst>=-1.650994 55 23 B (0.41818182 0.58181818) *
## 79) symmetry_worst< -1.650994 46 2 B (0.04347826 0.95652174) *
## 5) texture_worst< 4.572846 388 170 B (0.43814433 0.56185567)
## 10) texture_worst< 4.54138 343 167 B (0.48688047 0.51311953)
## 20) smoothness_worst>=-1.451731 63 16 M (0.74603175 0.25396825)
## 40) compactness_se>=-4.086695 55 8 M (0.85454545 0.14545455)
## 80) symmetry_worst< -1.395041 46 3 M (0.93478261 0.06521739) *
## 81) symmetry_worst>=-1.395041 9 4 B (0.44444444 0.55555556) *
## 41) compactness_se< -4.086695 8 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.451731 280 120 B (0.42857143 0.57142857)
## 42) symmetry_worst< -2.401622 17 0 M (1.00000000 0.00000000) *
## 43) symmetry_worst>=-2.401622 263 103 B (0.39163498 0.60836502)
## 86) texture_worst>=4.535341 14 0 M (1.00000000 0.00000000) *
## 87) texture_worst< 4.535341 249 89 B (0.35742972 0.64257028) *
## 11) texture_worst>=4.54138 45 3 B (0.06666667 0.93333333)
## 22) smoothness_mean>=-2.282906 6 3 M (0.50000000 0.50000000)
## 44) texture_mean>=2.943507 3 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.943507 3 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.282906 39 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.546123 17 0 B (0.00000000 1.00000000) *
##
## $trees[[68]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 400 M (0.56140351 0.43859649)
## 2) compactness_se>=-3.721197 376 131 M (0.65159574 0.34840426)
## 4) symmetry_worst>=-1.892495 256 69 M (0.73046875 0.26953125)
## 8) texture_worst>=3.969009 249 63 M (0.74698795 0.25301205)
## 16) compactness_se< -3.494301 97 13 M (0.86597938 0.13402062)
## 32) smoothness_mean>=-2.380711 63 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.380711 34 13 M (0.61764706 0.38235294)
## 66) compactness_se< -3.5866 25 4 M (0.84000000 0.16000000) *
## 67) compactness_se>=-3.5866 9 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.494301 152 50 M (0.67105263 0.32894737)
## 34) compactness_se>=-3.484318 131 29 M (0.77862595 0.22137405)
## 68) compactness_se< -2.919705 112 19 M (0.83035714 0.16964286) *
## 69) compactness_se>=-2.919705 19 9 B (0.47368421 0.52631579) *
## 35) compactness_se< -3.484318 21 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 3.969009 7 1 B (0.14285714 0.85714286)
## 18) texture_mean< 2.366153 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean>=2.366153 6 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.892495 120 58 B (0.48333333 0.51666667)
## 10) symmetry_worst< -1.982941 78 25 M (0.67948718 0.32051282)
## 20) symmetry_worst>=-2.174839 40 4 M (0.90000000 0.10000000)
## 40) texture_mean< 3.304787 38 2 M (0.94736842 0.05263158)
## 80) smoothness_worst>=-1.604936 32 0 M (1.00000000 0.00000000) *
## 81) smoothness_worst< -1.604936 6 2 M (0.66666667 0.33333333) *
## 41) texture_mean>=3.304787 2 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -2.174839 38 17 B (0.44736842 0.55263158)
## 42) smoothness_mean< -2.437515 13 1 M (0.92307692 0.07692308)
## 84) smoothness_mean>=-2.490273 12 0 M (1.00000000 0.00000000) *
## 85) smoothness_mean< -2.490273 1 0 B (0.00000000 1.00000000) *
## 43) smoothness_mean>=-2.437515 25 5 B (0.20000000 0.80000000)
## 86) texture_mean>=3.190563 9 4 M (0.55555556 0.44444444) *
## 87) texture_mean< 3.190563 16 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.982941 42 5 B (0.11904762 0.88095238)
## 22) texture_mean>=3.088324 5 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.088324 37 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 536 267 B (0.49813433 0.50186567)
## 6) compactness_se< -3.859436 458 209 M (0.54366812 0.45633188)
## 12) texture_mean>=2.934384 269 100 M (0.62825279 0.37174721)
## 24) compactness_se>=-4.28781 168 45 M (0.73214286 0.26785714)
## 48) smoothness_mean< -2.290664 130 23 M (0.82307692 0.17692308)
## 96) compactness_se< -3.869459 125 18 M (0.85600000 0.14400000) *
## 97) compactness_se>=-3.869459 5 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.290664 38 16 B (0.42105263 0.57894737)
## 98) smoothness_mean>=-2.251921 15 2 M (0.86666667 0.13333333) *
## 99) smoothness_mean< -2.251921 23 3 B (0.13043478 0.86956522) *
## 25) compactness_se< -4.28781 101 46 B (0.45544554 0.54455446)
## 50) texture_mean< 3.227241 71 25 M (0.64788732 0.35211268)
## 100) compactness_se< -4.335534 64 18 M (0.71875000 0.28125000) *
## 101) compactness_se>=-4.335534 7 0 B (0.00000000 1.00000000) *
## 51) texture_mean>=3.227241 30 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.934384 189 80 B (0.42328042 0.57671958)
## 26) texture_mean< 2.898946 155 77 B (0.49677419 0.50322581)
## 52) texture_mean>=2.876103 48 10 M (0.79166667 0.20833333)
## 104) symmetry_worst< -1.701169 41 3 M (0.92682927 0.07317073) *
## 105) symmetry_worst>=-1.701169 7 0 B (0.00000000 1.00000000) *
## 53) texture_mean< 2.876103 107 39 B (0.36448598 0.63551402)
## 106) smoothness_worst>=-1.542984 68 29 M (0.57352941 0.42647059) *
## 107) smoothness_worst< -1.542984 39 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.898946 34 3 B (0.08823529 0.91176471)
## 54) texture_worst>=4.707428 2 0 M (1.00000000 0.00000000) *
## 55) texture_worst< 4.707428 32 1 B (0.03125000 0.96875000)
## 110) compactness_se< -4.680858 1 0 M (1.00000000 0.00000000) *
## 111) compactness_se>=-4.680858 31 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.859436 78 18 B (0.23076923 0.76923077)
## 14) smoothness_worst>=-1.480731 33 15 M (0.54545455 0.45454545)
## 28) texture_mean>=2.971675 12 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.971675 21 6 B (0.28571429 0.71428571)
## 58) symmetry_worst>=-1.612049 6 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.612049 15 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.480731 45 0 B (0.00000000 1.00000000) *
##
## $trees[[69]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 455 B (0.49890351 0.50109649)
## 2) smoothness_mean>=-2.416986 622 279 M (0.55144695 0.44855305)
## 4) smoothness_mean< -2.349943 198 63 M (0.68181818 0.31818182)
## 8) symmetry_worst>=-2.212871 182 47 M (0.74175824 0.25824176)
## 16) texture_worst>=4.613791 108 11 M (0.89814815 0.10185185)
## 32) smoothness_mean>=-2.408892 104 7 M (0.93269231 0.06730769)
## 64) texture_mean< 3.36829 96 2 M (0.97916667 0.02083333) *
## 65) texture_mean>=3.36829 8 3 B (0.37500000 0.62500000) *
## 33) smoothness_mean< -2.408892 4 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.613791 74 36 M (0.51351351 0.48648649)
## 34) texture_mean>=2.97527 18 1 M (0.94444444 0.05555556)
## 68) texture_mean< 3.041522 17 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.041522 1 0 B (0.00000000 1.00000000) *
## 35) texture_mean< 2.97527 56 21 B (0.37500000 0.62500000)
## 70) smoothness_worst>=-1.545556 29 8 M (0.72413793 0.27586207) *
## 71) smoothness_worst< -1.545556 27 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.212871 16 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.349943 424 208 B (0.49056604 0.50943396)
## 10) smoothness_mean>=-2.332582 378 171 M (0.54761905 0.45238095)
## 20) compactness_se>=-3.027402 41 3 M (0.92682927 0.07317073)
## 40) compactness_se< -2.455682 38 1 M (0.97368421 0.02631579)
## 80) smoothness_worst>=-1.554815 31 0 M (1.00000000 0.00000000) *
## 81) smoothness_worst< -1.554815 7 1 M (0.85714286 0.14285714) *
## 41) compactness_se>=-2.455682 3 1 B (0.33333333 0.66666667)
## 82) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 2.915767 2 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.027402 337 168 M (0.50148368 0.49851632)
## 42) smoothness_worst< -1.562856 27 1 M (0.96296296 0.03703704)
## 84) smoothness_mean< -2.277089 23 0 M (1.00000000 0.00000000) *
## 85) smoothness_mean>=-2.277089 4 1 M (0.75000000 0.25000000) *
## 43) smoothness_worst>=-1.562856 310 143 B (0.46129032 0.53870968)
## 86) texture_worst>=5.073596 13 0 M (1.00000000 0.00000000) *
## 87) texture_worst< 5.073596 297 130 B (0.43771044 0.56228956) *
## 11) smoothness_mean< -2.332582 46 1 B (0.02173913 0.97826087)
## 22) symmetry_worst< -2.154356 1 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst>=-2.154356 45 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.416986 290 112 B (0.38620690 0.61379310)
## 6) compactness_se>=-4.350232 200 94 B (0.47000000 0.53000000)
## 12) compactness_se< -4.283814 28 1 M (0.96428571 0.03571429)
## 24) smoothness_worst>=-1.654625 27 0 M (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.654625 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-4.283814 172 67 B (0.38953488 0.61046512)
## 26) texture_worst>=5.003123 29 6 M (0.79310345 0.20689655)
## 52) smoothness_mean>=-2.492372 26 3 M (0.88461538 0.11538462)
## 104) texture_worst< 5.316369 20 0 M (1.00000000 0.00000000) *
## 105) texture_worst>=5.316369 6 3 M (0.50000000 0.50000000) *
## 53) smoothness_mean< -2.492372 3 0 B (0.00000000 1.00000000) *
## 27) texture_worst< 5.003123 143 44 B (0.30769231 0.69230769)
## 54) smoothness_worst< -1.598711 59 28 M (0.52542373 0.47457627)
## 108) symmetry_worst>=-2.050132 40 11 M (0.72500000 0.27500000) *
## 109) symmetry_worst< -2.050132 19 2 B (0.10526316 0.89473684) *
## 55) smoothness_worst>=-1.598711 84 13 B (0.15476190 0.84523810)
## 110) symmetry_worst< -1.995409 6 1 M (0.83333333 0.16666667) *
## 111) symmetry_worst>=-1.995409 78 8 B (0.10256410 0.89743590) *
## 7) compactness_se< -4.350232 90 18 B (0.20000000 0.80000000)
## 14) texture_mean>=3.124472 21 10 B (0.47619048 0.52380952)
## 28) texture_mean< 3.17309 10 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.17309 11 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.124472 69 8 B (0.11594203 0.88405797)
## 30) symmetry_worst>=-1.658507 26 7 B (0.26923077 0.73076923)
## 60) smoothness_mean< -2.503847 4 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.503847 22 3 B (0.13636364 0.86363636)
## 122) texture_mean< 2.936149 4 1 M (0.75000000 0.25000000) *
## 123) texture_mean>=2.936149 18 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.658507 43 1 B (0.02325581 0.97674419)
## 62) compactness_se< -4.6643 6 1 B (0.16666667 0.83333333)
## 124) compactness_se>=-4.740419 1 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -4.740419 5 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-4.6643 37 0 B (0.00000000 1.00000000) *
##
## $trees[[70]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 444 M (0.51315789 0.48684211)
## 2) smoothness_mean>=-2.332634 422 163 M (0.61374408 0.38625592)
## 4) smoothness_mean< -2.31481 70 10 M (0.85714286 0.14285714)
## 8) texture_mean>=2.849464 56 2 M (0.96428571 0.03571429)
## 16) compactness_se< -3.515615 42 0 M (1.00000000 0.00000000) *
## 17) compactness_se>=-3.515615 14 2 M (0.85714286 0.14285714)
## 34) compactness_se>=-3.342347 12 0 M (1.00000000 0.00000000) *
## 35) compactness_se< -3.342347 2 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.849464 14 6 B (0.42857143 0.57142857)
## 18) smoothness_mean>=-2.322851 6 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean< -2.322851 8 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.31481 352 153 M (0.56534091 0.43465909)
## 10) compactness_se>=-4.222363 312 119 M (0.61858974 0.38141026)
## 20) smoothness_mean>=-2.303285 292 99 M (0.66095890 0.33904110)
## 40) symmetry_worst>=-1.775265 181 45 M (0.75138122 0.24861878)
## 80) texture_mean>=2.777879 159 32 M (0.79874214 0.20125786) *
## 81) texture_mean< 2.777879 22 9 B (0.40909091 0.59090909) *
## 41) symmetry_worst< -1.775265 111 54 M (0.51351351 0.48648649)
## 82) smoothness_worst< -1.433708 94 38 M (0.59574468 0.40425532) *
## 83) smoothness_worst>=-1.433708 17 1 B (0.05882353 0.94117647) *
## 21) smoothness_mean< -2.303285 20 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.222363 40 6 B (0.15000000 0.85000000)
## 22) smoothness_mean< -2.3007 6 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.3007 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.332634 490 209 B (0.42653061 0.57346939)
## 6) smoothness_mean< -2.349943 451 204 B (0.45232816 0.54767184)
## 12) smoothness_mean>=-2.357834 19 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.357834 432 185 B (0.42824074 0.57175926)
## 26) texture_mean>=2.763153 411 185 B (0.45012165 0.54987835)
## 52) texture_worst< 4.3976 64 21 M (0.67187500 0.32812500)
## 104) smoothness_worst>=-1.554805 33 2 M (0.93939394 0.06060606) *
## 105) smoothness_worst< -1.554805 31 12 B (0.38709677 0.61290323) *
## 53) texture_worst>=4.3976 347 142 B (0.40922190 0.59077810)
## 106) smoothness_mean>=-2.396732 78 30 M (0.61538462 0.38461538) *
## 107) smoothness_mean< -2.396732 269 94 B (0.34944238 0.65055762) *
## 27) texture_mean< 2.763153 21 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.349943 39 5 B (0.12820513 0.87179487)
## 14) smoothness_worst>=-1.435092 2 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst< -1.435092 37 3 B (0.08108108 0.91891892)
## 30) symmetry_worst< -2.189951 3 1 M (0.66666667 0.33333333)
## 60) texture_mean< 3.025767 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.025767 1 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.189951 34 1 B (0.02941176 0.97058824)
## 62) symmetry_worst>=-1.41845 7 1 B (0.14285714 0.85714286)
## 124) texture_mean>=2.986903 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.986903 6 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.41845 27 0 B (0.00000000 1.00000000) *
##
## $trees[[71]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.51096491 0.48903509)
## 2) symmetry_worst>=-1.840831 571 247 M (0.56742557 0.43257443)
## 4) compactness_se>=-3.690481 250 83 M (0.66800000 0.33200000)
## 8) smoothness_worst< -1.434262 188 48 M (0.74468085 0.25531915)
## 16) symmetry_worst< -1.128751 167 32 M (0.80838323 0.19161677)
## 32) smoothness_mean>=-2.503795 163 28 M (0.82822086 0.17177914)
## 64) texture_worst>=4.56463 93 8 M (0.91397849 0.08602151) *
## 65) texture_worst< 4.56463 70 20 M (0.71428571 0.28571429) *
## 33) smoothness_mean< -2.503795 4 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.128751 21 5 B (0.23809524 0.76190476)
## 34) symmetry_worst>=-1.068249 5 0 M (1.00000000 0.00000000) *
## 35) symmetry_worst< -1.068249 16 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.434262 62 27 B (0.43548387 0.56451613)
## 18) compactness_se< -3.470851 17 2 M (0.88235294 0.11764706)
## 36) texture_mean>=2.688296 15 0 M (1.00000000 0.00000000) *
## 37) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-3.470851 45 12 B (0.26666667 0.73333333)
## 38) symmetry_worst>=-1.306254 5 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.306254 40 7 B (0.17500000 0.82500000)
## 78) texture_worst< 4.846274 21 7 B (0.33333333 0.66666667) *
## 79) texture_worst>=4.846274 19 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.690481 321 157 B (0.48909657 0.51090343)
## 10) smoothness_worst< -1.576769 53 13 M (0.75471698 0.24528302)
## 20) smoothness_mean< -2.496118 34 2 M (0.94117647 0.05882353)
## 40) texture_mean< 3.17207 32 0 M (1.00000000 0.00000000) *
## 41) texture_mean>=3.17207 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.496118 19 8 B (0.42105263 0.57894737)
## 42) smoothness_mean>=-2.434747 6 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.434747 13 2 B (0.15384615 0.84615385)
## 86) texture_mean>=3.31519 1 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.31519 12 1 B (0.08333333 0.91666667) *
## 11) smoothness_worst>=-1.576769 268 117 B (0.43656716 0.56343284)
## 22) smoothness_worst>=-1.556321 236 117 B (0.49576271 0.50423729)
## 44) compactness_se< -3.859436 200 89 M (0.55500000 0.44500000)
## 88) smoothness_mean>=-2.473387 190 79 M (0.58421053 0.41578947) *
## 89) smoothness_mean< -2.473387 10 0 B (0.00000000 1.00000000) *
## 45) compactness_se>=-3.859436 36 6 B (0.16666667 0.83333333)
## 90) smoothness_worst>=-1.455217 8 2 M (0.75000000 0.25000000) *
## 91) smoothness_worst< -1.455217 28 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.556321 32 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.840831 341 142 B (0.41642229 0.58357771)
## 6) symmetry_worst< -1.925345 261 129 B (0.49425287 0.50574713)
## 12) symmetry_worst>=-2.052205 122 46 M (0.62295082 0.37704918)
## 24) smoothness_mean>=-2.449526 101 28 M (0.72277228 0.27722772)
## 48) smoothness_worst< -1.540225 51 1 M (0.98039216 0.01960784)
## 96) compactness_se< -3.451641 50 0 M (1.00000000 0.00000000) *
## 97) compactness_se>=-3.451641 1 0 B (0.00000000 1.00000000) *
## 49) smoothness_worst>=-1.540225 50 23 B (0.46000000 0.54000000)
## 98) symmetry_worst< -1.990435 19 4 M (0.78947368 0.21052632) *
## 99) symmetry_worst>=-1.990435 31 8 B (0.25806452 0.74193548) *
## 25) smoothness_mean< -2.449526 21 3 B (0.14285714 0.85714286)
## 50) smoothness_worst>=-1.503558 3 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst< -1.503558 18 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -2.052205 139 53 B (0.38129496 0.61870504)
## 26) compactness_se>=-3.487878 36 10 M (0.72222222 0.27777778)
## 52) texture_mean>=3.049609 28 2 M (0.92857143 0.07142857)
## 104) smoothness_mean>=-2.661875 27 1 M (0.96296296 0.03703704) *
## 105) smoothness_mean< -2.661875 1 0 B (0.00000000 1.00000000) *
## 53) texture_mean< 3.049609 8 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.487878 103 27 B (0.26213592 0.73786408)
## 54) smoothness_mean>=-2.30775 25 6 M (0.76000000 0.24000000)
## 108) smoothness_worst>=-1.497846 14 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst< -1.497846 11 5 B (0.45454545 0.54545455) *
## 55) smoothness_mean< -2.30775 78 8 B (0.10256410 0.89743590)
## 110) smoothness_worst>=-1.549837 28 8 B (0.28571429 0.71428571) *
## 111) smoothness_worst< -1.549837 50 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.925345 80 13 B (0.16250000 0.83750000)
## 14) texture_worst>=4.937311 13 3 M (0.76923077 0.23076923)
## 28) compactness_se>=-4.899363 10 0 M (1.00000000 0.00000000) *
## 29) compactness_se< -4.899363 3 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.937311 67 3 B (0.04477612 0.95522388)
## 30) smoothness_worst>=-1.424105 2 1 M (0.50000000 0.50000000)
## 60) texture_mean< 2.876957 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.876957 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.424105 65 2 B (0.03076923 0.96923077)
## 62) smoothness_mean< -2.386198 14 2 B (0.14285714 0.85714286)
## 124) smoothness_mean>=-2.406561 2 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.406561 12 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.386198 51 0 B (0.00000000 1.00000000) *
##
## $trees[[72]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) texture_mean>=2.892314 675 335 B (0.49629630 0.50370370)
## 4) symmetry_worst< -2.379234 18 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-2.379234 657 317 B (0.48249619 0.51750381)
## 10) smoothness_mean< -2.473552 106 36 M (0.66037736 0.33962264)
## 20) texture_mean>=2.935975 97 27 M (0.72164948 0.27835052)
## 40) texture_mean< 3.15715 72 11 M (0.84722222 0.15277778)
## 80) compactness_se< -2.82386 69 8 M (0.88405797 0.11594203) *
## 81) compactness_se>=-2.82386 3 0 B (0.00000000 1.00000000) *
## 41) texture_mean>=3.15715 25 9 B (0.36000000 0.64000000)
## 82) smoothness_mean>=-2.489159 10 1 M (0.90000000 0.10000000) *
## 83) smoothness_mean< -2.489159 15 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.935975 9 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.473552 551 247 B (0.44827586 0.55172414)
## 22) smoothness_worst>=-1.609811 516 246 B (0.47674419 0.52325581)
## 44) symmetry_worst>=-2.193154 471 234 M (0.50318471 0.49681529)
## 88) symmetry_worst< -2.115313 18 0 M (1.00000000 0.00000000) *
## 89) symmetry_worst>=-2.115313 453 219 B (0.48344371 0.51655629) *
## 45) symmetry_worst< -2.193154 45 9 B (0.20000000 0.80000000)
## 90) smoothness_mean< -2.447413 5 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean>=-2.447413 40 4 B (0.10000000 0.90000000) *
## 23) smoothness_worst< -1.609811 35 1 B (0.02857143 0.97142857)
## 46) smoothness_mean>=-2.337942 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.337942 34 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.892314 237 75 B (0.31645570 0.68354430)
## 6) compactness_se>=-3.891799 118 53 B (0.44915254 0.55084746)
## 12) smoothness_worst>=-1.482701 62 24 M (0.61290323 0.38709677)
## 24) symmetry_worst>=-1.732707 37 7 M (0.81081081 0.18918919)
## 48) symmetry_worst< -1.395292 20 0 M (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.395292 17 7 M (0.58823529 0.41176471)
## 98) symmetry_worst>=-1.281003 11 1 M (0.90909091 0.09090909) *
## 99) symmetry_worst< -1.281003 6 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.732707 25 8 B (0.32000000 0.68000000)
## 50) smoothness_worst< -1.478176 8 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst>=-1.478176 17 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.482701 56 15 B (0.26785714 0.73214286)
## 26) texture_worst< 3.919786 15 5 M (0.66666667 0.33333333)
## 52) smoothness_mean>=-2.461945 11 1 M (0.90909091 0.09090909)
## 104) smoothness_mean< -2.298096 10 0 M (1.00000000 0.00000000) *
## 105) smoothness_mean>=-2.298096 1 0 B (0.00000000 1.00000000) *
## 53) smoothness_mean< -2.461945 4 0 B (0.00000000 1.00000000) *
## 27) texture_worst>=3.919786 41 5 B (0.12195122 0.87804878)
## 54) texture_mean< 2.717337 6 1 M (0.83333333 0.16666667)
## 108) symmetry_worst>=-1.940832 5 0 M (1.00000000 0.00000000) *
## 109) symmetry_worst< -1.940832 1 0 B (0.00000000 1.00000000) *
## 55) texture_mean>=2.717337 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.891799 119 22 B (0.18487395 0.81512605)
## 14) compactness_se< -4.159844 54 18 B (0.33333333 0.66666667)
## 28) compactness_se>=-4.198706 14 2 M (0.85714286 0.14285714)
## 56) texture_mean>=2.772165 12 0 M (1.00000000 0.00000000) *
## 57) texture_mean< 2.772165 2 0 B (0.00000000 1.00000000) *
## 29) compactness_se< -4.198706 40 6 B (0.15000000 0.85000000)
## 58) smoothness_worst< -1.546636 7 1 M (0.85714286 0.14285714)
## 116) texture_mean>=2.85389 6 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.85389 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst>=-1.546636 33 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.159844 65 4 B (0.06153846 0.93846154)
## 30) smoothness_worst>=-1.451541 8 4 M (0.50000000 0.50000000)
## 60) texture_mean>=2.803301 4 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.803301 4 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.451541 57 0 B (0.00000000 1.00000000) *
##
## $trees[[73]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 411 M (0.54934211 0.45065789)
## 2) texture_mean>=2.892591 690 269 M (0.61014493 0.38985507)
## 4) texture_worst< 4.753106 382 117 M (0.69371728 0.30628272)
## 8) texture_mean>=3.055881 73 7 M (0.90410959 0.09589041)
## 16) compactness_se>=-4.572499 70 4 M (0.94285714 0.05714286)
## 32) smoothness_worst>=-1.606352 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst< -1.606352 16 4 M (0.75000000 0.25000000)
## 66) smoothness_worst< -1.693722 12 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst>=-1.693722 4 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.572499 3 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 3.055881 309 110 M (0.64401294 0.35598706)
## 18) smoothness_worst>=-1.473478 53 5 M (0.90566038 0.09433962)
## 36) texture_mean>=2.934384 49 2 M (0.95918367 0.04081633)
## 72) texture_mean< 3.039982 46 0 M (1.00000000 0.00000000) *
## 73) texture_mean>=3.039982 3 1 B (0.33333333 0.66666667) *
## 37) texture_mean< 2.934384 4 1 B (0.25000000 0.75000000)
## 74) smoothness_mean< -2.240603 1 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean>=-2.240603 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.473478 256 105 M (0.58984375 0.41015625)
## 38) smoothness_worst< -1.476997 239 88 M (0.63179916 0.36820084)
## 76) smoothness_mean< -2.234468 204 62 M (0.69607843 0.30392157) *
## 77) smoothness_mean>=-2.234468 35 9 B (0.25714286 0.74285714) *
## 39) smoothness_worst>=-1.476997 17 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.753106 308 152 M (0.50649351 0.49350649)
## 10) texture_worst>=4.818867 254 107 M (0.57874016 0.42125984)
## 20) smoothness_mean>=-2.462871 198 66 M (0.66666667 0.33333333)
## 40) symmetry_worst>=-2.207988 184 53 M (0.71195652 0.28804348)
## 80) smoothness_worst< -1.447185 130 21 M (0.83846154 0.16153846) *
## 81) smoothness_worst>=-1.447185 54 22 B (0.40740741 0.59259259) *
## 41) symmetry_worst< -2.207988 14 1 B (0.07142857 0.92857143)
## 82) smoothness_mean>=-2.282229 1 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.282229 13 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.462871 56 15 B (0.26785714 0.73214286)
## 42) symmetry_worst< -1.635915 27 12 M (0.55555556 0.44444444)
## 84) texture_mean< 3.17309 14 2 M (0.85714286 0.14285714) *
## 85) texture_mean>=3.17309 13 3 B (0.23076923 0.76923077) *
## 43) symmetry_worst>=-1.635915 29 0 B (0.00000000 1.00000000) *
## 11) texture_worst< 4.818867 54 9 B (0.16666667 0.83333333)
## 22) symmetry_worst>=-0.9904278 4 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst< -0.9904278 50 5 B (0.10000000 0.90000000)
## 46) compactness_se>=-3.322755 4 1 M (0.75000000 0.25000000)
## 92) smoothness_mean>=-2.522464 3 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean< -2.522464 1 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.322755 46 2 B (0.04347826 0.95652174)
## 94) texture_worst< 4.781945 9 2 B (0.22222222 0.77777778) *
## 95) texture_worst>=4.781945 37 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.892591 222 80 B (0.36036036 0.63963964)
## 6) smoothness_mean>=-2.31958 117 58 M (0.50427350 0.49572650)
## 12) texture_mean< 2.844609 71 23 M (0.67605634 0.32394366)
## 24) texture_worst>=4.1745 40 6 M (0.85000000 0.15000000)
## 48) symmetry_worst>=-1.987693 38 4 M (0.89473684 0.10526316)
## 96) smoothness_mean< -2.238735 22 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.238735 16 4 M (0.75000000 0.25000000) *
## 49) symmetry_worst< -1.987693 2 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.1745 31 14 B (0.45161290 0.54838710)
## 50) smoothness_mean< -2.298594 7 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.298594 24 7 B (0.29166667 0.70833333)
## 102) symmetry_worst>=-1.612049 10 3 M (0.70000000 0.30000000) *
## 103) symmetry_worst< -1.612049 14 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=2.844609 46 11 B (0.23913043 0.76086957)
## 26) texture_worst>=4.669441 4 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.669441 42 7 B (0.16666667 0.83333333)
## 54) texture_worst< 4.361241 11 4 M (0.63636364 0.36363636)
## 108) texture_mean>=2.857891 8 1 M (0.87500000 0.12500000) *
## 109) texture_mean< 2.857891 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.361241 31 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.31958 105 21 B (0.20000000 0.80000000)
## 14) compactness_se< -4.31315 21 10 M (0.52380952 0.47619048)
## 28) texture_mean>=2.871852 8 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.871852 13 3 B (0.23076923 0.76923077)
## 58) smoothness_mean>=-2.372437 5 2 M (0.60000000 0.40000000)
## 116) texture_mean>=2.800736 3 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.800736 2 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.372437 8 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.31315 84 10 B (0.11904762 0.88095238)
## 30) smoothness_worst>=-1.452493 4 1 M (0.75000000 0.25000000)
## 60) texture_mean>=2.597803 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.597803 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.452493 80 7 B (0.08750000 0.91250000)
## 62) compactness_se>=-3.488718 22 7 B (0.31818182 0.68181818)
## 124) compactness_se< -3.483667 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.483667 18 3 B (0.16666667 0.83333333) *
## 63) compactness_se< -3.488718 58 0 B (0.00000000 1.00000000) *
##
## $trees[[74]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 447 B (0.49013158 0.50986842)
## 2) texture_mean>=2.963467 511 206 M (0.59686888 0.40313112)
## 4) smoothness_worst>=-1.579228 402 137 M (0.65920398 0.34079602)
## 8) smoothness_mean>=-2.501158 393 128 M (0.67430025 0.32569975)
## 16) smoothness_worst< -1.568787 27 0 M (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.568787 366 128 M (0.65027322 0.34972678)
## 34) smoothness_worst>=-1.561324 355 118 M (0.66760563 0.33239437)
## 68) smoothness_worst< -1.532606 55 6 M (0.89090909 0.10909091) *
## 69) smoothness_worst>=-1.532606 300 112 M (0.62666667 0.37333333) *
## 35) smoothness_worst< -1.561324 11 1 B (0.09090909 0.90909091)
## 70) compactness_se>=-2.682598 1 0 M (1.00000000 0.00000000) *
## 71) compactness_se< -2.682598 10 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.501158 9 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.579228 109 40 B (0.36697248 0.63302752)
## 10) symmetry_worst>=-1.693879 35 9 M (0.74285714 0.25714286)
## 20) compactness_se< -3.885202 21 0 M (1.00000000 0.00000000) *
## 21) compactness_se>=-3.885202 14 5 B (0.35714286 0.64285714)
## 42) compactness_se>=-3.153142 5 0 M (1.00000000 0.00000000) *
## 43) compactness_se< -3.153142 9 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.693879 74 14 B (0.18918919 0.81081081)
## 22) symmetry_worst< -2.081905 19 9 M (0.52631579 0.47368421)
## 44) compactness_se>=-3.424051 10 2 M (0.80000000 0.20000000)
## 88) smoothness_mean>=-2.638103 8 0 M (1.00000000 0.00000000) *
## 89) smoothness_mean< -2.638103 2 0 B (0.00000000 1.00000000) *
## 45) compactness_se< -3.424051 9 2 B (0.22222222 0.77777778)
## 90) smoothness_mean>=-2.373466 2 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.373466 7 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-2.081905 55 4 B (0.07272727 0.92727273)
## 46) texture_mean< 2.969886 2 0 M (1.00000000 0.00000000) *
## 47) texture_mean>=2.969886 53 2 B (0.03773585 0.96226415)
## 94) texture_mean>=3.172196 5 2 B (0.40000000 0.60000000) *
## 95) texture_mean< 3.172196 48 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.963467 401 142 B (0.35411471 0.64588529)
## 6) symmetry_worst>=-1.325507 22 3 M (0.86363636 0.13636364)
## 12) smoothness_mean>=-2.340715 20 1 M (0.95000000 0.05000000)
## 24) compactness_se< -2.588521 19 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-2.588521 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.340715 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.325507 379 123 B (0.32453826 0.67546174)
## 14) compactness_se< -3.426516 323 120 B (0.37151703 0.62848297)
## 28) compactness_se>=-3.764682 83 34 M (0.59036145 0.40963855)
## 56) symmetry_worst>=-1.813857 50 11 M (0.78000000 0.22000000)
## 112) texture_worst>=4.256309 30 0 M (1.00000000 0.00000000) *
## 113) texture_worst< 4.256309 20 9 B (0.45000000 0.55000000) *
## 57) symmetry_worst< -1.813857 33 10 B (0.30303030 0.69696970)
## 114) texture_worst< 4.000974 9 2 M (0.77777778 0.22222222) *
## 115) texture_worst>=4.000974 24 3 B (0.12500000 0.87500000) *
## 29) compactness_se< -3.764682 240 71 B (0.29583333 0.70416667)
## 58) smoothness_mean>=-2.391331 150 57 B (0.38000000 0.62000000)
## 116) texture_worst>=4.389172 100 48 M (0.52000000 0.48000000) *
## 117) texture_worst< 4.389172 50 5 B (0.10000000 0.90000000) *
## 59) smoothness_mean< -2.391331 90 14 B (0.15555556 0.84444444)
## 118) texture_worst< 4.411124 16 4 M (0.75000000 0.25000000) *
## 119) texture_worst>=4.411124 74 2 B (0.02702703 0.97297297) *
## 15) compactness_se>=-3.426516 56 3 B (0.05357143 0.94642857)
## 30) smoothness_mean>=-2.154617 4 2 M (0.50000000 0.50000000)
## 60) texture_mean>=2.720927 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.720927 2 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.154617 52 1 B (0.01923077 0.98076923)
## 62) symmetry_worst< -1.783471 6 1 B (0.16666667 0.83333333)
## 124) texture_mean>=2.902347 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.902347 5 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-1.783471 46 0 B (0.00000000 1.00000000) *
##
## $trees[[75]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 390 B (0.4276316 0.5723684)
## 2) texture_worst>=5.402766 22 0 M (1.0000000 0.0000000) *
## 3) texture_worst< 5.402766 890 368 B (0.4134831 0.5865169)
## 6) smoothness_mean>=-2.173316 62 18 M (0.7096774 0.2903226)
## 12) smoothness_worst< -1.409741 29 0 M (1.0000000 0.0000000) *
## 13) smoothness_worst>=-1.409741 33 15 B (0.4545455 0.5454545)
## 26) symmetry_worst>=-1.656121 22 7 M (0.6818182 0.3181818)
## 52) texture_mean>=2.688296 15 0 M (1.0000000 0.0000000) *
## 53) texture_mean< 2.688296 7 0 B (0.0000000 1.0000000) *
## 27) symmetry_worst< -1.656121 11 0 B (0.0000000 1.0000000) *
## 7) smoothness_mean< -2.173316 828 324 B (0.3913043 0.6086957)
## 14) symmetry_worst>=-1.001713 11 0 M (1.0000000 0.0000000) *
## 15) symmetry_worst< -1.001713 817 313 B (0.3831089 0.6168911)
## 30) compactness_se>=-4.505325 733 299 B (0.4079127 0.5920873)
## 60) compactness_se< -4.49319 14 0 M (1.0000000 0.0000000) *
## 61) compactness_se>=-4.49319 719 285 B (0.3963839 0.6036161)
## 122) texture_worst>=4.905415 138 59 M (0.5724638 0.4275362) *
## 123) texture_worst< 4.905415 581 206 B (0.3545611 0.6454389) *
## 31) compactness_se< -4.505325 84 14 B (0.1666667 0.8333333)
## 62) symmetry_worst< -2.374205 6 0 M (1.0000000 0.0000000) *
## 63) symmetry_worst>=-2.374205 78 8 B (0.1025641 0.8974359)
## 126) smoothness_mean< -2.449246 28 8 B (0.2857143 0.7142857) *
## 127) smoothness_mean>=-2.449246 50 0 B (0.0000000 1.0000000) *
##
## $trees[[76]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 B (0.48026316 0.51973684)
## 2) compactness_se>=-4.694501 893 438 B (0.49048152 0.50951848)
## 4) texture_worst>=4.275472 782 381 M (0.51278772 0.48721228)
## 8) smoothness_mean>=-2.201842 46 10 M (0.78260870 0.21739130)
## 16) smoothness_mean< -2.093138 40 5 M (0.87500000 0.12500000)
## 32) compactness_se>=-4.24572 38 3 M (0.92105263 0.07894737)
## 64) texture_mean>=2.918531 31 0 M (1.00000000 0.00000000) *
## 65) texture_mean< 2.918531 7 3 M (0.57142857 0.42857143) *
## 33) compactness_se< -4.24572 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.093138 6 1 B (0.16666667 0.83333333)
## 34) texture_mean< 2.894137 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=2.894137 5 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.201842 736 365 B (0.49592391 0.50407609)
## 18) smoothness_mean< -2.235394 690 332 M (0.51884058 0.48115942)
## 36) texture_worst< 4.550789 191 60 M (0.68586387 0.31413613)
## 72) compactness_se< -2.751692 180 49 M (0.72777778 0.27222222) *
## 73) compactness_se>=-2.751692 11 0 B (0.00000000 1.00000000) *
## 37) texture_worst>=4.550789 499 227 B (0.45490982 0.54509018)
## 74) texture_worst>=4.572846 457 223 B (0.48796499 0.51203501) *
## 75) texture_worst< 4.572846 42 4 B (0.09523810 0.90476190) *
## 19) smoothness_mean>=-2.235394 46 7 B (0.15217391 0.84782609)
## 38) texture_mean>=3.04949 4 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 3.04949 42 3 B (0.07142857 0.92857143)
## 78) texture_worst< 4.329277 1 0 M (1.00000000 0.00000000) *
## 79) texture_worst>=4.329277 41 2 B (0.04878049 0.95121951) *
## 5) texture_worst< 4.275472 111 37 B (0.33333333 0.66666667)
## 10) texture_worst< 4.18243 70 34 B (0.48571429 0.51428571)
## 20) compactness_se>=-3.97985 59 25 M (0.57627119 0.42372881)
## 40) compactness_se< -3.48221 41 12 M (0.70731707 0.29268293)
## 80) smoothness_mean>=-2.466148 37 8 M (0.78378378 0.21621622) *
## 81) smoothness_mean< -2.466148 4 0 B (0.00000000 1.00000000) *
## 41) compactness_se>=-3.48221 18 5 B (0.27777778 0.72222222)
## 82) smoothness_worst>=-1.490036 5 0 M (1.00000000 0.00000000) *
## 83) smoothness_worst< -1.490036 13 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.97985 11 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.18243 41 3 B (0.07317073 0.92682927)
## 22) texture_mean< 2.715026 3 1 M (0.66666667 0.33333333)
## 44) texture_mean>=2.710705 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.710705 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=2.715026 38 1 B (0.02631579 0.97368421)
## 46) smoothness_mean>=-2.15202 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.15202 37 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -4.694501 19 0 B (0.00000000 1.00000000) *
##
## $trees[[77]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 M (0.51754386 0.48245614)
## 2) compactness_se>=-4.198706 699 310 M (0.55650930 0.44349070)
## 4) texture_worst>=4.481821 528 209 M (0.60416667 0.39583333)
## 8) symmetry_worst>=-1.43353 43 4 M (0.90697674 0.09302326)
## 16) texture_mean< 3.110611 37 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.110611 6 2 B (0.33333333 0.66666667)
## 34) texture_mean>=3.146047 2 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.146047 4 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.43353 485 205 M (0.57731959 0.42268041)
## 18) compactness_se< -4.116284 38 3 M (0.92105263 0.07894737)
## 36) smoothness_mean>=-2.434347 35 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean< -2.434347 3 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-4.116284 447 202 M (0.54809843 0.45190157)
## 38) texture_mean>=2.892591 426 183 M (0.57042254 0.42957746)
## 76) compactness_se< -4.094455 19 0 M (1.00000000 0.00000000) *
## 77) compactness_se>=-4.094455 407 183 M (0.55036855 0.44963145) *
## 39) texture_mean< 2.892591 21 2 B (0.09523810 0.90476190)
## 78) smoothness_worst>=-1.459092 2 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.459092 19 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.481821 171 70 B (0.40935673 0.59064327)
## 10) texture_mean< 2.760642 60 21 M (0.65000000 0.35000000)
## 20) smoothness_mean>=-2.360495 50 11 M (0.78000000 0.22000000)
## 40) compactness_se>=-3.943187 47 8 M (0.82978723 0.17021277)
## 80) symmetry_worst< -1.461208 42 5 M (0.88095238 0.11904762) *
## 81) symmetry_worst>=-1.461208 5 2 B (0.40000000 0.60000000) *
## 41) compactness_se< -3.943187 3 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.360495 10 0 B (0.00000000 1.00000000) *
## 11) texture_mean>=2.760642 111 31 B (0.27927928 0.72072072)
## 22) compactness_se< -4.160164 8 0 M (1.00000000 0.00000000) *
## 23) compactness_se>=-4.160164 103 23 B (0.22330097 0.77669903)
## 46) texture_worst< 4.034664 5 0 M (1.00000000 0.00000000) *
## 47) texture_worst>=4.034664 98 18 B (0.18367347 0.81632653)
## 94) compactness_se>=-3.294139 11 4 M (0.63636364 0.36363636) *
## 95) compactness_se< -3.294139 87 11 B (0.12643678 0.87356322) *
## 3) compactness_se< -4.198706 213 83 B (0.38967136 0.61032864)
## 6) smoothness_mean< -2.3007 176 81 B (0.46022727 0.53977273)
## 12) texture_mean< 3.217018 147 69 M (0.53061224 0.46938776)
## 24) texture_mean>=2.960617 85 26 M (0.69411765 0.30588235)
## 48) texture_worst< 4.984637 60 10 M (0.83333333 0.16666667)
## 96) symmetry_worst>=-2.046832 56 6 M (0.89285714 0.10714286) *
## 97) symmetry_worst< -2.046832 4 0 B (0.00000000 1.00000000) *
## 49) texture_worst>=4.984637 25 9 B (0.36000000 0.64000000)
## 98) texture_mean>=3.12836 9 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.12836 16 0 B (0.00000000 1.00000000) *
## 25) texture_mean< 2.960617 62 19 B (0.30645161 0.69354839)
## 50) compactness_se< -4.327955 41 19 B (0.46341463 0.53658537)
## 100) compactness_se>=-4.356557 8 0 M (1.00000000 0.00000000) *
## 101) compactness_se< -4.356557 33 11 B (0.33333333 0.66666667) *
## 51) compactness_se>=-4.327955 21 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=3.217018 29 3 B (0.10344828 0.89655172)
## 26) texture_mean>=3.388429 3 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 3.388429 26 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.3007 37 2 B (0.05405405 0.94594595)
## 14) smoothness_worst>=-1.435212 2 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst< -1.435212 35 0 B (0.00000000 1.00000000) *
##
## $trees[[78]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 393 B (0.43092105 0.56907895)
## 2) symmetry_worst>=-2.20425 838 378 B (0.45107399 0.54892601)
## 4) smoothness_mean>=-2.423454 584 289 B (0.49486301 0.50513699)
## 8) symmetry_worst< -1.925345 103 27 M (0.73786408 0.26213592)
## 16) symmetry_worst>=-1.966829 41 2 M (0.95121951 0.04878049)
## 32) texture_mean>=2.753964 40 1 M (0.97500000 0.02500000)
## 64) smoothness_mean< -2.225218 39 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.225218 1 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.753964 1 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.966829 62 25 M (0.59677419 0.40322581)
## 34) texture_worst>=4.85229 18 0 M (1.00000000 0.00000000) *
## 35) texture_worst< 4.85229 44 19 B (0.43181818 0.56818182)
## 70) smoothness_worst< -1.471555 34 15 M (0.55882353 0.44117647) *
## 71) smoothness_worst>=-1.471555 10 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.925345 481 213 B (0.44282744 0.55717256)
## 18) symmetry_worst>=-1.839419 421 205 B (0.48693587 0.51306413)
## 36) compactness_se>=-3.66733 169 63 M (0.62721893 0.37278107)
## 72) compactness_se< -3.494301 45 4 M (0.91111111 0.08888889) *
## 73) compactness_se>=-3.494301 124 59 M (0.52419355 0.47580645) *
## 37) compactness_se< -3.66733 252 99 B (0.39285714 0.60714286)
## 74) symmetry_worst< -1.749307 60 24 M (0.60000000 0.40000000) *
## 75) symmetry_worst>=-1.749307 192 63 B (0.32812500 0.67187500) *
## 19) symmetry_worst< -1.839419 60 8 B (0.13333333 0.86666667)
## 38) texture_worst>=4.927821 4 0 M (1.00000000 0.00000000) *
## 39) texture_worst< 4.927821 56 4 B (0.07142857 0.92857143)
## 78) smoothness_mean< -2.352223 8 4 M (0.50000000 0.50000000) *
## 79) smoothness_mean>=-2.352223 48 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.423454 254 89 B (0.35039370 0.64960630)
## 10) smoothness_mean< -2.454106 161 76 B (0.47204969 0.52795031)
## 20) symmetry_worst>=-1.54778 27 2 M (0.92592593 0.07407407)
## 40) texture_mean>=2.91613 25 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.91613 2 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.54778 134 51 B (0.38059701 0.61940299)
## 42) symmetry_worst< -1.617873 111 51 B (0.45945946 0.54054054)
## 84) symmetry_worst>=-1.844742 63 20 M (0.68253968 0.31746032) *
## 85) symmetry_worst< -1.844742 48 8 B (0.16666667 0.83333333) *
## 43) symmetry_worst>=-1.617873 23 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.454106 93 13 B (0.13978495 0.86021505)
## 22) texture_worst< 4.536807 15 7 M (0.53333333 0.46666667)
## 44) smoothness_worst< -1.595541 8 0 M (1.00000000 0.00000000) *
## 45) smoothness_worst>=-1.595541 7 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.536807 78 5 B (0.06410256 0.93589744)
## 46) symmetry_worst< -1.993222 13 5 B (0.38461538 0.61538462)
## 92) smoothness_worst>=-1.525709 4 0 M (1.00000000 0.00000000) *
## 93) smoothness_worst< -1.525709 9 1 B (0.11111111 0.88888889) *
## 47) symmetry_worst>=-1.993222 65 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.20425 74 15 B (0.20270270 0.79729730)
## 6) compactness_se>=-3.487878 16 6 M (0.62500000 0.37500000)
## 12) compactness_se< -3.248462 11 1 M (0.90909091 0.09090909)
## 24) texture_mean>=2.822066 10 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.822066 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.248462 5 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.487878 58 5 B (0.08620690 0.91379310)
## 14) compactness_se< -4.480041 8 3 M (0.62500000 0.37500000)
## 28) smoothness_mean< -2.271294 5 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.271294 3 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.480041 50 0 B (0.00000000 1.00000000) *
##
## $trees[[79]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 423 B (0.46381579 0.53618421)
## 2) texture_mean>=2.963467 526 247 M (0.53041825 0.46958175)
## 4) symmetry_worst>=-1.067772 13 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst< -1.067772 513 247 M (0.51851852 0.48148148)
## 10) smoothness_mean< -2.093138 500 234 M (0.53200000 0.46800000)
## 20) compactness_se>=-4.706178 489 223 M (0.54396728 0.45603272)
## 40) symmetry_worst< -1.132261 478 212 M (0.55648536 0.44351464)
## 80) symmetry_worst>=-1.41845 19 0 M (1.00000000 0.00000000) *
## 81) symmetry_worst< -1.41845 459 212 M (0.53812636 0.46187364) *
## 41) symmetry_worst>=-1.132261 11 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.706178 11 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.093138 13 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.963467 386 144 B (0.37305699 0.62694301)
## 6) smoothness_mean>=-2.333148 197 93 B (0.47208122 0.52791878)
## 12) smoothness_worst< -1.477389 111 46 M (0.58558559 0.41441441)
## 24) smoothness_worst>=-1.482701 27 1 M (0.96296296 0.03703704)
## 48) texture_mean< 2.893521 26 0 M (1.00000000 0.00000000) *
## 49) texture_mean>=2.893521 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.482701 84 39 B (0.46428571 0.53571429)
## 50) smoothness_worst< -1.567043 14 1 M (0.92857143 0.07142857)
## 100) smoothness_mean>=-2.310275 13 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean< -2.310275 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.567043 70 26 B (0.37142857 0.62857143)
## 102) texture_worst>=4.522453 29 11 M (0.62068966 0.37931034) *
## 103) texture_worst< 4.522453 41 8 B (0.19512195 0.80487805) *
## 13) smoothness_worst>=-1.477389 86 28 B (0.32558140 0.67441860)
## 26) texture_mean>=2.934384 13 1 M (0.92307692 0.07692308)
## 52) texture_worst< 4.599229 12 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=4.599229 1 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 2.934384 73 16 B (0.21917808 0.78082192)
## 54) symmetry_worst>=-1.36527 15 6 M (0.60000000 0.40000000)
## 108) texture_mean>=2.706904 10 1 M (0.90000000 0.10000000) *
## 109) texture_mean< 2.706904 5 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.36527 58 7 B (0.12068966 0.87931034)
## 110) texture_mean< 2.515298 3 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.515298 55 4 B (0.07272727 0.92727273) *
## 7) smoothness_mean< -2.333148 189 51 B (0.26984127 0.73015873)
## 14) compactness_se< -4.650552 18 3 M (0.83333333 0.16666667)
## 28) smoothness_mean< -2.441817 15 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.441817 3 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.650552 171 36 B (0.21052632 0.78947368)
## 30) smoothness_worst>=-1.472307 22 10 M (0.54545455 0.45454545)
## 60) symmetry_worst>=-1.64088 13 2 M (0.84615385 0.15384615)
## 120) texture_mean>=2.735974 11 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.735974 2 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst< -1.64088 9 1 B (0.11111111 0.88888889)
## 122) smoothness_mean>=-2.363458 1 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.363458 8 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.472307 149 24 B (0.16107383 0.83892617)
## 62) symmetry_worst< -1.692331 91 24 B (0.26373626 0.73626374)
## 124) symmetry_worst>=-1.815934 40 20 M (0.50000000 0.50000000) *
## 125) symmetry_worst< -1.815934 51 4 B (0.07843137 0.92156863) *
## 63) symmetry_worst>=-1.692331 58 0 B (0.00000000 1.00000000) *
##
## $trees[[80]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 367 B (0.40241228 0.59758772)
## 2) symmetry_worst>=-1.322543 38 10 M (0.73684211 0.26315789)
## 4) compactness_se< -2.646661 34 6 M (0.82352941 0.17647059)
## 8) smoothness_worst>=-1.497484 27 1 M (0.96296296 0.03703704)
## 16) texture_mean>=2.644674 26 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.644674 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.497484 7 2 B (0.28571429 0.71428571)
## 18) texture_mean>=3.126045 2 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.126045 5 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-2.646661 4 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.322543 874 339 B (0.38787185 0.61212815)
## 6) compactness_se< -4.098353 231 112 B (0.48484848 0.51515152)
## 12) smoothness_mean< -2.291157 203 94 M (0.53694581 0.46305419)
## 24) smoothness_mean>=-2.368246 45 8 M (0.82222222 0.17777778)
## 48) symmetry_worst< -1.476085 41 4 M (0.90243902 0.09756098)
## 96) smoothness_mean< -2.299097 32 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.299097 9 4 M (0.55555556 0.44444444) *
## 49) symmetry_worst>=-1.476085 4 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.368246 158 72 B (0.45569620 0.54430380)
## 50) texture_worst< 4.984637 132 62 M (0.53030303 0.46969697)
## 100) texture_mean>=2.976294 49 9 M (0.81632653 0.18367347) *
## 101) texture_mean< 2.976294 83 30 B (0.36144578 0.63855422) *
## 51) texture_worst>=4.984637 26 2 B (0.07692308 0.92307692)
## 102) compactness_se>=-4.265617 1 0 M (1.00000000 0.00000000) *
## 103) compactness_se< -4.265617 25 1 B (0.04000000 0.96000000) *
## 13) smoothness_mean>=-2.291157 28 3 B (0.10714286 0.89285714)
## 26) texture_worst>=4.59101 6 3 M (0.50000000 0.50000000)
## 52) smoothness_mean>=-2.22149 3 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.22149 3 0 B (0.00000000 1.00000000) *
## 27) texture_worst< 4.59101 22 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-4.098353 643 227 B (0.35303266 0.64696734)
## 14) compactness_se>=-4.022675 581 223 B (0.38382100 0.61617900)
## 28) smoothness_worst>=-1.499656 248 119 M (0.52016129 0.47983871)
## 56) smoothness_mean>=-2.288684 135 45 M (0.66666667 0.33333333)
## 112) texture_mean>=2.920399 94 17 M (0.81914894 0.18085106) *
## 113) texture_mean< 2.920399 41 13 B (0.31707317 0.68292683) *
## 57) smoothness_mean< -2.288684 113 39 B (0.34513274 0.65486726)
## 114) symmetry_worst>=-1.431268 9 0 M (1.00000000 0.00000000) *
## 115) symmetry_worst< -1.431268 104 30 B (0.28846154 0.71153846) *
## 29) smoothness_worst< -1.499656 333 94 B (0.28228228 0.71771772)
## 58) smoothness_worst< -1.515751 263 91 B (0.34600760 0.65399240)
## 116) compactness_se>=-3.738233 190 82 B (0.43157895 0.56842105) *
## 117) compactness_se< -3.738233 73 9 B (0.12328767 0.87671233) *
## 59) smoothness_worst>=-1.515751 70 3 B (0.04285714 0.95714286)
## 118) compactness_se< -3.450179 17 3 B (0.17647059 0.82352941) *
## 119) compactness_se>=-3.450179 53 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -4.022675 62 4 B (0.06451613 0.93548387)
## 30) texture_mean>=3.112668 4 0 M (1.00000000 0.00000000) *
## 31) texture_mean< 3.112668 58 0 B (0.00000000 1.00000000) *
##
## $trees[[81]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 402 B (0.44078947 0.55921053)
## 2) compactness_se>=-3.812868 475 237 B (0.49894737 0.50105263)
## 4) smoothness_worst< -1.400053 416 193 M (0.53605769 0.46394231)
## 8) smoothness_worst>=-1.450407 25 1 M (0.96000000 0.04000000)
## 16) smoothness_mean>=-2.420336 24 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.420336 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.450407 391 192 M (0.50895141 0.49104859)
## 18) smoothness_worst< -1.532606 164 63 M (0.61585366 0.38414634)
## 36) smoothness_worst>=-1.586874 81 18 M (0.77777778 0.22222222)
## 72) texture_mean< 3.168177 68 9 M (0.86764706 0.13235294) *
## 73) texture_mean>=3.168177 13 4 B (0.30769231 0.69230769) *
## 37) smoothness_worst< -1.586874 83 38 B (0.45783133 0.54216867)
## 74) smoothness_worst< -1.59459 68 30 M (0.55882353 0.44117647) *
## 75) smoothness_worst>=-1.59459 15 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.532606 227 98 B (0.43171806 0.56828194)
## 38) smoothness_mean< -2.453563 18 0 M (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.453563 209 80 B (0.38277512 0.61722488)
## 78) smoothness_mean>=-2.367658 161 76 B (0.47204969 0.52795031) *
## 79) smoothness_mean< -2.367658 48 4 B (0.08333333 0.91666667) *
## 5) smoothness_worst>=-1.400053 59 14 B (0.23728814 0.76271186)
## 10) smoothness_worst>=-1.395608 31 14 B (0.45161290 0.54838710)
## 20) smoothness_mean< -2.194024 8 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean>=-2.194024 23 6 B (0.26086957 0.73913043)
## 42) symmetry_worst>=-1.596878 9 3 M (0.66666667 0.33333333)
## 84) texture_mean>=2.688296 6 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 2.688296 3 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst< -1.596878 14 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.395608 28 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.812868 437 165 B (0.37757437 0.62242563)
## 6) compactness_se< -3.867535 394 163 B (0.41370558 0.58629442)
## 12) compactness_se>=-3.883925 24 1 M (0.95833333 0.04166667)
## 24) texture_mean>=2.689116 23 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.689116 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -3.883925 370 140 B (0.37837838 0.62162162)
## 26) texture_mean>=2.803607 341 140 B (0.41055718 0.58944282)
## 52) compactness_se< -3.935037 300 135 B (0.45000000 0.55000000)
## 104) compactness_se>=-3.977364 18 1 M (0.94444444 0.05555556) *
## 105) compactness_se< -3.977364 282 118 B (0.41843972 0.58156028) *
## 53) compactness_se>=-3.935037 41 5 B (0.12195122 0.87804878)
## 106) smoothness_mean>=-2.240561 4 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.240561 37 1 B (0.02702703 0.97297297) *
## 27) texture_mean< 2.803607 29 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.867535 43 2 B (0.04651163 0.95348837)
## 14) smoothness_worst>=-1.464982 4 2 M (0.50000000 0.50000000)
## 28) texture_mean< 2.90276 2 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.90276 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.464982 39 0 B (0.00000000 1.00000000) *
##
## $trees[[82]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 M (0.52412281 0.47587719)
## 2) symmetry_worst>=-1.641484 356 133 M (0.62640449 0.37359551)
## 4) smoothness_mean>=-2.320393 169 40 M (0.76331361 0.23668639)
## 8) compactness_se< -2.780114 162 33 M (0.79629630 0.20370370)
## 16) compactness_se>=-4.463708 157 28 M (0.82165605 0.17834395)
## 32) smoothness_mean< -2.301086 34 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.301086 123 28 M (0.77235772 0.22764228)
## 66) smoothness_mean>=-2.296604 118 23 M (0.80508475 0.19491525) *
## 67) smoothness_mean< -2.296604 5 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.463708 5 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.780114 7 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.320393 187 93 M (0.50267380 0.49732620)
## 10) compactness_se< -4.178455 62 14 M (0.77419355 0.22580645)
## 20) texture_mean< 3.110176 50 6 M (0.88000000 0.12000000)
## 40) texture_mean>=2.906784 47 3 M (0.93617021 0.06382979)
## 80) texture_worst>=4.340304 46 2 M (0.95652174 0.04347826) *
## 81) texture_worst< 4.340304 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.906784 3 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=3.110176 12 4 B (0.33333333 0.66666667)
## 42) texture_worst>=5.204837 4 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.204837 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.178455 125 46 B (0.36800000 0.63200000)
## 22) texture_worst>=4.993407 13 0 M (1.00000000 0.00000000) *
## 23) texture_worst< 4.993407 112 33 B (0.29464286 0.70535714)
## 46) symmetry_worst< -1.638169 14 1 M (0.92857143 0.07142857)
## 92) texture_mean>=2.7241 13 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.7241 1 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst>=-1.638169 98 20 B (0.20408163 0.79591837)
## 94) symmetry_worst>=-1.429489 19 8 M (0.57894737 0.42105263) *
## 95) symmetry_worst< -1.429489 79 9 B (0.11392405 0.88607595) *
## 3) symmetry_worst< -1.641484 556 255 B (0.45863309 0.54136691)
## 6) smoothness_mean< -2.237735 479 238 M (0.50313152 0.49686848)
## 12) smoothness_mean>=-2.257137 21 1 M (0.95238095 0.04761905)
## 24) compactness_se>=-3.898257 20 0 M (1.00000000 0.00000000) *
## 25) compactness_se< -3.898257 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.257137 458 221 B (0.48253275 0.51746725)
## 26) symmetry_worst< -1.750623 349 163 M (0.53295129 0.46704871)
## 52) texture_worst>=4.897936 81 23 M (0.71604938 0.28395062)
## 104) symmetry_worst>=-2.257286 75 17 M (0.77333333 0.22666667) *
## 105) symmetry_worst< -2.257286 6 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.897936 268 128 B (0.47761194 0.52238806)
## 106) texture_worst< 4.751358 239 112 M (0.53138075 0.46861925) *
## 107) texture_worst>=4.751358 29 1 B (0.03448276 0.96551724) *
## 27) symmetry_worst>=-1.750623 109 35 B (0.32110092 0.67889908)
## 54) texture_mean>=2.955415 71 35 B (0.49295775 0.50704225)
## 108) symmetry_worst>=-1.716495 27 3 M (0.88888889 0.11111111) *
## 109) symmetry_worst< -1.716495 44 11 B (0.25000000 0.75000000) *
## 55) texture_mean< 2.955415 38 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.237735 77 14 B (0.18181818 0.81818182)
## 14) smoothness_worst< -1.56036 3 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst>=-1.56036 74 11 B (0.14864865 0.85135135)
## 30) texture_mean>=3.044046 12 6 M (0.50000000 0.50000000)
## 60) smoothness_mean< -2.120284 6 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.120284 6 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.044046 62 5 B (0.08064516 0.91935484)
## 62) compactness_se>=-3.011681 2 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -3.011681 60 3 B (0.05000000 0.95000000)
## 126) compactness_se< -4.140724 10 3 B (0.30000000 0.70000000) *
## 127) compactness_se>=-4.140724 50 0 B (0.00000000 1.00000000) *
##
## $trees[[83]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 419 B (0.45942982 0.54057018)
## 2) symmetry_worst>=-1.658814 361 161 M (0.55401662 0.44598338)
## 4) texture_worst>=4.605737 211 68 M (0.67772512 0.32227488)
## 8) symmetry_worst< -1.606972 49 5 M (0.89795918 0.10204082)
## 16) texture_mean< 3.14185 43 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.14185 6 1 B (0.16666667 0.83333333)
## 34) texture_mean>=3.244071 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.244071 5 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.606972 162 63 M (0.61111111 0.38888889)
## 18) symmetry_worst>=-1.591238 151 52 M (0.65562914 0.34437086)
## 36) texture_worst>=4.993407 30 2 M (0.93333333 0.06666667)
## 72) compactness_se>=-4.507761 28 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.507761 2 0 B (0.00000000 1.00000000) *
## 37) texture_worst< 4.993407 121 50 M (0.58677686 0.41322314)
## 74) compactness_se< -3.768789 65 15 M (0.76923077 0.23076923) *
## 75) compactness_se>=-3.768789 56 21 B (0.37500000 0.62500000) *
## 19) symmetry_worst< -1.591238 11 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.605737 150 57 B (0.38000000 0.62000000)
## 10) smoothness_mean>=-2.171581 28 4 M (0.85714286 0.14285714)
## 20) compactness_se>=-3.95959 26 2 M (0.92307692 0.07692308)
## 40) smoothness_worst< -1.333822 23 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst>=-1.333822 3 1 B (0.33333333 0.66666667)
## 82) texture_mean>=2.688296 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.95959 2 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.171581 122 33 B (0.27049180 0.72950820)
## 22) smoothness_worst>=-1.472112 43 21 B (0.48837209 0.51162791)
## 44) symmetry_worst< -1.397194 17 2 M (0.88235294 0.11764706)
## 88) texture_worst>=4.110502 15 0 M (1.00000000 0.00000000) *
## 89) texture_worst< 4.110502 2 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.397194 26 6 B (0.23076923 0.76923077)
## 90) texture_worst< 4.074625 5 0 M (1.00000000 0.00000000) *
## 91) texture_worst>=4.074625 21 1 B (0.04761905 0.95238095) *
## 23) smoothness_worst< -1.472112 79 12 B (0.15189873 0.84810127)
## 46) texture_mean>=2.975525 9 2 M (0.77777778 0.22222222)
## 92) smoothness_mean< -2.275789 7 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean>=-2.275789 2 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 2.975525 70 5 B (0.07142857 0.92857143)
## 94) smoothness_worst>=-1.496237 21 5 B (0.23809524 0.76190476) *
## 95) smoothness_worst< -1.496237 49 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.658814 551 219 B (0.39745917 0.60254083)
## 6) symmetry_worst< -1.681676 525 219 B (0.41714286 0.58285714)
## 12) smoothness_worst< -1.476214 417 192 B (0.46043165 0.53956835)
## 24) smoothness_worst>=-1.482699 31 2 M (0.93548387 0.06451613)
## 48) compactness_se>=-3.967101 27 0 M (1.00000000 0.00000000) *
## 49) compactness_se< -3.967101 4 2 M (0.50000000 0.50000000)
## 98) texture_mean>=2.981733 2 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.981733 2 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.482699 386 163 B (0.42227979 0.57772021)
## 50) symmetry_worst>=-1.815934 122 50 M (0.59016393 0.40983607)
## 100) smoothness_worst< -1.484675 104 32 M (0.69230769 0.30769231) *
## 101) smoothness_worst>=-1.484675 18 0 B (0.00000000 1.00000000) *
## 51) symmetry_worst< -1.815934 264 91 B (0.34469697 0.65530303)
## 102) texture_worst>=4.624749 102 48 M (0.52941176 0.47058824) *
## 103) texture_worst< 4.624749 162 37 B (0.22839506 0.77160494) *
## 13) smoothness_worst>=-1.476214 108 27 B (0.25000000 0.75000000)
## 26) compactness_se>=-3.294139 11 3 M (0.72727273 0.27272727)
## 52) texture_mean< 3.23593 8 0 M (1.00000000 0.00000000) *
## 53) texture_mean>=3.23593 3 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.294139 97 19 B (0.19587629 0.80412371)
## 54) texture_worst< 4.624204 40 14 B (0.35000000 0.65000000)
## 108) texture_worst>=4.373034 15 1 M (0.93333333 0.06666667) *
## 109) texture_worst< 4.373034 25 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.624204 57 5 B (0.08771930 0.91228070)
## 110) texture_mean>=3.207548 2 0 M (1.00000000 0.00000000) *
## 111) texture_mean< 3.207548 55 3 B (0.05454545 0.94545455) *
## 7) symmetry_worst>=-1.681676 26 0 B (0.00000000 1.00000000) *
##
## $trees[[84]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 413 B (0.45285088 0.54714912)
## 2) symmetry_worst>=-1.580867 257 111 M (0.56809339 0.43190661)
## 4) texture_worst>=4.477941 193 64 M (0.66839378 0.33160622)
## 8) symmetry_worst< -1.557842 25 0 M (1.00000000 0.00000000) *
## 9) symmetry_worst>=-1.557842 168 64 M (0.61904762 0.38095238)
## 18) symmetry_worst>=-1.549706 153 49 M (0.67973856 0.32026144)
## 36) smoothness_worst< -1.513087 58 8 M (0.86206897 0.13793103)
## 72) texture_mean>=2.904002 55 5 M (0.90909091 0.09090909) *
## 73) texture_mean< 2.904002 3 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst>=-1.513087 95 41 M (0.56842105 0.43157895)
## 74) smoothness_mean>=-2.277448 38 5 M (0.86842105 0.13157895) *
## 75) smoothness_mean< -2.277448 57 21 B (0.36842105 0.63157895) *
## 19) symmetry_worst< -1.549706 15 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.477941 64 17 B (0.26562500 0.73437500)
## 10) smoothness_worst>=-1.496237 36 17 B (0.47222222 0.52777778)
## 20) texture_mean>=2.803301 13 1 M (0.92307692 0.07692308)
## 40) compactness_se< -2.679301 12 0 M (1.00000000 0.00000000) *
## 41) compactness_se>=-2.679301 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.803301 23 5 B (0.21739130 0.78260870)
## 42) texture_mean< 2.531355 3 0 M (1.00000000 0.00000000) *
## 43) texture_mean>=2.531355 20 2 B (0.10000000 0.90000000)
## 86) compactness_se>=-3.1317 2 0 M (1.00000000 0.00000000) *
## 87) compactness_se< -3.1317 18 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.496237 28 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.580867 655 267 B (0.40763359 0.59236641)
## 6) texture_mean>=3.431382 14 0 M (1.00000000 0.00000000) *
## 7) texture_mean< 3.431382 641 253 B (0.39469579 0.60530421)
## 14) texture_mean< 3.21023 557 236 B (0.42369838 0.57630162)
## 28) texture_worst>=5.016194 22 3 M (0.86363636 0.13636364)
## 56) texture_worst< 5.280287 19 0 M (1.00000000 0.00000000) *
## 57) texture_worst>=5.280287 3 0 B (0.00000000 1.00000000) *
## 29) texture_worst< 5.016194 535 217 B (0.40560748 0.59439252)
## 58) compactness_se< -3.391153 449 199 B (0.44320713 0.55679287)
## 116) compactness_se>=-3.772915 172 69 M (0.59883721 0.40116279) *
## 117) compactness_se< -3.772915 277 96 B (0.34657040 0.65342960) *
## 59) compactness_se>=-3.391153 86 18 B (0.20930233 0.79069767)
## 118) texture_mean>=3.038537 26 9 M (0.65384615 0.34615385) *
## 119) texture_mean< 3.038537 60 1 B (0.01666667 0.98333333) *
## 15) texture_mean>=3.21023 84 17 B (0.20238095 0.79761905)
## 30) symmetry_worst>=-1.709835 5 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst< -1.709835 79 12 B (0.15189873 0.84810127)
## 62) compactness_se>=-3.424051 5 1 M (0.80000000 0.20000000)
## 124) smoothness_mean< -2.457972 4 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.457972 1 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.424051 74 8 B (0.10810811 0.89189189)
## 126) smoothness_worst>=-1.435634 2 0 M (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.435634 72 6 B (0.08333333 0.91666667) *
##
## $trees[[85]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 B (0.48464912 0.51535088)
## 2) smoothness_mean>=-2.392182 553 246 M (0.55515371 0.44484629)
## 4) smoothness_mean< -2.38347 31 0 M (1.00000000 0.00000000) *
## 5) smoothness_mean>=-2.38347 522 246 M (0.52873563 0.47126437)
## 10) smoothness_mean>=-2.380331 509 233 M (0.54223969 0.45776031)
## 20) smoothness_worst< -1.562856 50 9 M (0.82000000 0.18000000)
## 40) smoothness_worst>=-1.574324 18 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.574324 32 9 M (0.71875000 0.28125000)
## 82) smoothness_mean>=-2.337942 24 3 M (0.87500000 0.12500000) *
## 83) smoothness_mean< -2.337942 8 2 B (0.25000000 0.75000000) *
## 21) smoothness_worst>=-1.562856 459 224 M (0.51198257 0.48801743)
## 42) symmetry_worst>=-2.151948 422 192 M (0.54502370 0.45497630)
## 84) compactness_se>=-4.50262 406 176 M (0.56650246 0.43349754) *
## 85) compactness_se< -4.50262 16 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst< -2.151948 37 5 B (0.13513514 0.86486486)
## 86) compactness_se>=-3.382349 4 1 M (0.75000000 0.25000000) *
## 87) compactness_se< -3.382349 33 2 B (0.06060606 0.93939394) *
## 11) smoothness_mean< -2.380331 13 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.392182 359 135 B (0.37604457 0.62395543)
## 6) compactness_se< -3.941776 156 72 M (0.53846154 0.46153846)
## 12) smoothness_mean>=-2.422045 44 6 M (0.86363636 0.13636364)
## 24) smoothness_mean< -2.399979 39 1 M (0.97435897 0.02564103)
## 48) symmetry_worst< -1.677281 33 0 M (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.677281 6 1 M (0.83333333 0.16666667)
## 98) compactness_se>=-4.280953 5 0 M (1.00000000 0.00000000) *
## 99) compactness_se< -4.280953 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.399979 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.422045 112 46 B (0.41071429 0.58928571)
## 26) smoothness_mean< -2.443803 83 38 M (0.54216867 0.45783133)
## 52) symmetry_worst>=-1.562165 19 1 M (0.94736842 0.05263158)
## 104) texture_mean>=2.84952 18 0 M (1.00000000 0.00000000) *
## 105) texture_mean< 2.84952 1 0 B (0.00000000 1.00000000) *
## 53) symmetry_worst< -1.562165 64 27 B (0.42187500 0.57812500)
## 106) smoothness_worst>=-1.638322 52 25 M (0.51923077 0.48076923) *
## 107) smoothness_worst< -1.638322 12 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean>=-2.443803 29 1 B (0.03448276 0.96551724)
## 54) smoothness_worst< -1.607486 2 1 M (0.50000000 0.50000000)
## 108) texture_mean>=2.884013 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.884013 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst>=-1.607486 27 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.941776 203 51 B (0.25123153 0.74876847)
## 14) smoothness_mean< -2.461054 88 42 B (0.47727273 0.52272727)
## 28) smoothness_worst>=-1.556752 16 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.556752 72 26 B (0.36111111 0.63888889)
## 58) symmetry_worst< -2.106078 18 2 M (0.88888889 0.11111111)
## 116) texture_mean>=3.076827 16 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 3.076827 2 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst>=-2.106078 54 10 B (0.18518519 0.81481481)
## 118) symmetry_worst>=-1.816662 30 10 B (0.33333333 0.66666667) *
## 119) symmetry_worst< -1.816662 24 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.461054 115 9 B (0.07826087 0.92173913)
## 30) texture_mean< 2.788514 7 2 M (0.71428571 0.28571429)
## 60) texture_mean>=2.735767 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.735767 2 0 B (0.00000000 1.00000000) *
## 31) texture_mean>=2.788514 108 4 B (0.03703704 0.96296296)
## 62) symmetry_worst>=-1.783406 34 4 B (0.11764706 0.88235294)
## 124) symmetry_worst< -1.685469 4 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.685469 30 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.783406 74 0 B (0.00000000 1.00000000) *
##
## $trees[[86]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 363 B (0.39802632 0.60197368)
## 2) compactness_se< -4.098353 266 127 M (0.52255639 0.47744361)
## 4) smoothness_mean< -2.299097 212 84 M (0.60377358 0.39622642)
## 8) smoothness_mean>=-2.426508 111 16 M (0.85585586 0.14414414)
## 16) texture_mean>=2.799406 108 13 M (0.87962963 0.12037037)
## 32) smoothness_worst>=-1.555906 80 3 M (0.96250000 0.03750000)
## 64) symmetry_worst>=-2.212871 78 1 M (0.98717949 0.01282051) *
## 65) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 33) smoothness_worst< -1.555906 28 10 M (0.64285714 0.35714286)
## 66) smoothness_worst< -1.567258 21 3 M (0.85714286 0.14285714) *
## 67) smoothness_worst>=-1.567258 7 0 B (0.00000000 1.00000000) *
## 17) texture_mean< 2.799406 3 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.426508 101 33 B (0.32673267 0.67326733)
## 18) symmetry_worst>=-1.695215 32 12 M (0.62500000 0.37500000)
## 36) smoothness_mean< -2.449649 24 4 M (0.83333333 0.16666667)
## 72) smoothness_worst< -1.549205 21 1 M (0.95238095 0.04761905) *
## 73) smoothness_worst>=-1.549205 3 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.449649 8 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.695215 69 13 B (0.18840580 0.81159420)
## 38) smoothness_worst>=-1.55307 15 7 M (0.53333333 0.46666667)
## 76) smoothness_mean< -2.440656 8 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean>=-2.440656 7 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst< -1.55307 54 5 B (0.09259259 0.90740741)
## 78) texture_mean< 2.969886 16 5 B (0.31250000 0.68750000) *
## 79) texture_mean>=2.969886 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.299097 54 11 B (0.20370370 0.79629630)
## 10) compactness_se>=-4.222363 27 11 B (0.40740741 0.59259259)
## 20) compactness_se< -4.178775 12 1 M (0.91666667 0.08333333)
## 40) smoothness_worst>=-1.49438 11 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.49438 1 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.178775 15 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.222363 27 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-4.098353 646 224 B (0.34674923 0.65325077)
## 6) smoothness_mean>=-2.394871 470 190 B (0.40425532 0.59574468)
## 12) compactness_se>=-4.025757 425 190 B (0.44705882 0.55294118)
## 24) texture_worst>=4.895983 65 20 M (0.69230769 0.30769231)
## 48) symmetry_worst>=-2.207988 55 10 M (0.81818182 0.18181818)
## 96) texture_mean< 3.36829 49 4 M (0.91836735 0.08163265) *
## 97) texture_mean>=3.36829 6 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst< -2.207988 10 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.895983 360 145 B (0.40277778 0.59722222)
## 50) texture_worst< 4.782287 302 136 B (0.45033113 0.54966887)
## 100) smoothness_mean< -2.366217 21 2 M (0.90476190 0.09523810) *
## 101) smoothness_mean>=-2.366217 281 117 B (0.41637011 0.58362989) *
## 51) texture_worst>=4.782287 58 9 B (0.15517241 0.84482759)
## 102) compactness_se>=-2.785754 7 0 M (1.00000000 0.00000000) *
## 103) compactness_se< -2.785754 51 2 B (0.03921569 0.96078431) *
## 13) compactness_se< -4.025757 45 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.394871 176 34 B (0.19318182 0.80681818)
## 14) symmetry_worst>=-1.466953 8 2 M (0.75000000 0.25000000)
## 28) texture_worst< 4.774321 5 0 M (1.00000000 0.00000000) *
## 29) texture_worst>=4.774321 3 1 B (0.33333333 0.66666667)
## 58) texture_mean>=3.044129 1 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.044129 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.466953 168 28 B (0.16666667 0.83333333)
## 30) smoothness_worst< -1.598711 56 19 B (0.33928571 0.66071429)
## 60) smoothness_worst>=-1.653746 18 5 M (0.72222222 0.27777778)
## 120) compactness_se< -3.268044 14 1 M (0.92857143 0.07142857) *
## 121) compactness_se>=-3.268044 4 0 B (0.00000000 1.00000000) *
## 61) smoothness_worst< -1.653746 38 6 B (0.15789474 0.84210526)
## 122) compactness_se>=-2.979429 8 2 M (0.75000000 0.25000000) *
## 123) compactness_se< -2.979429 30 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.598711 112 9 B (0.08035714 0.91964286)
## 62) texture_worst>=5.19153 4 1 M (0.75000000 0.25000000)
## 124) smoothness_mean< -2.473552 3 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.473552 1 0 B (0.00000000 1.00000000) *
## 63) texture_worst< 5.19153 108 6 B (0.05555556 0.94444444)
## 126) texture_worst>=4.568716 42 6 B (0.14285714 0.85714286) *
## 127) texture_worst< 4.568716 66 0 B (0.00000000 1.00000000) *
##
## $trees[[87]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 451 M (0.50548246 0.49451754)
## 2) smoothness_mean>=-2.423454 682 305 M (0.55278592 0.44721408)
## 4) smoothness_worst< -1.4768 397 150 M (0.62216625 0.37783375)
## 8) smoothness_worst>=-1.482107 52 2 M (0.96153846 0.03846154)
## 16) texture_worst>=4.126187 50 0 M (1.00000000 0.00000000) *
## 17) texture_worst< 4.126187 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482107 345 148 M (0.57101449 0.42898551)
## 18) compactness_se< -4.100467 83 21 M (0.74698795 0.25301205)
## 36) smoothness_mean< -2.3007 75 13 M (0.82666667 0.17333333)
## 72) texture_mean>=2.809289 71 9 M (0.87323944 0.12676056) *
## 73) texture_mean< 2.809289 4 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.3007 8 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-4.100467 262 127 M (0.51526718 0.48473282)
## 38) symmetry_worst>=-1.835199 156 56 M (0.64102564 0.35897436)
## 76) texture_worst>=4.57172 78 14 M (0.82051282 0.17948718) *
## 77) texture_worst< 4.57172 78 36 B (0.46153846 0.53846154) *
## 39) symmetry_worst< -1.835199 106 35 B (0.33018868 0.66981132)
## 78) symmetry_worst< -2.923662 9 0 M (1.00000000 0.00000000) *
## 79) symmetry_worst>=-2.923662 97 26 B (0.26804124 0.73195876) *
## 5) smoothness_worst>=-1.4768 285 130 B (0.45614035 0.54385965)
## 10) smoothness_worst>=-1.473476 251 121 M (0.51792829 0.48207171)
## 20) symmetry_worst>=-1.721298 160 57 M (0.64375000 0.35625000)
## 40) compactness_se>=-4.04059 112 27 M (0.75892857 0.24107143)
## 80) smoothness_mean>=-2.359377 97 13 M (0.86597938 0.13402062) *
## 81) smoothness_mean< -2.359377 15 1 B (0.06666667 0.93333333) *
## 41) compactness_se< -4.04059 48 18 B (0.37500000 0.62500000)
## 82) smoothness_mean< -2.294648 21 3 M (0.85714286 0.14285714) *
## 83) smoothness_mean>=-2.294648 27 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.721298 91 27 B (0.29670330 0.70329670)
## 42) texture_worst< 4.623467 23 6 M (0.73913043 0.26086957)
## 84) texture_mean>=2.84692 18 1 M (0.94444444 0.05555556) *
## 85) texture_mean< 2.84692 5 0 B (0.00000000 1.00000000) *
## 43) texture_worst>=4.623467 68 10 B (0.14705882 0.85294118)
## 86) symmetry_worst< -1.820896 18 8 M (0.55555556 0.44444444) *
## 87) symmetry_worst>=-1.820896 50 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473476 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.423454 230 84 B (0.36521739 0.63478261)
## 6) smoothness_mean< -2.441446 198 83 B (0.41919192 0.58080808)
## 12) symmetry_worst>=-1.54778 21 4 M (0.80952381 0.19047619)
## 24) smoothness_mean>=-2.487591 18 1 M (0.94444444 0.05555556)
## 48) texture_mean>=2.844831 17 0 M (1.00000000 0.00000000) *
## 49) texture_mean< 2.844831 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.487591 3 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.54778 177 66 B (0.37288136 0.62711864)
## 26) symmetry_worst< -1.750953 132 60 B (0.45454545 0.54545455)
## 52) symmetry_worst>=-1.868413 47 15 M (0.68085106 0.31914894)
## 104) texture_mean< 2.977229 32 4 M (0.87500000 0.12500000) *
## 105) texture_mean>=2.977229 15 4 B (0.26666667 0.73333333) *
## 53) symmetry_worst< -1.868413 85 28 B (0.32941176 0.67058824)
## 106) compactness_se>=-3.514597 44 20 M (0.54545455 0.45454545) *
## 107) compactness_se< -3.514597 41 4 B (0.09756098 0.90243902) *
## 27) symmetry_worst>=-1.750953 45 6 B (0.13333333 0.86666667)
## 54) texture_mean>=2.958874 17 6 B (0.35294118 0.64705882)
## 108) compactness_se< -4.196102 7 1 M (0.85714286 0.14285714) *
## 109) compactness_se>=-4.196102 10 0 B (0.00000000 1.00000000) *
## 55) texture_mean< 2.958874 28 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.441446 32 1 B (0.03125000 0.96875000)
## 14) smoothness_worst< -1.607486 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst>=-1.607486 31 0 B (0.00000000 1.00000000) *
##
## $trees[[88]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 M (0.50438596 0.49561404)
## 2) smoothness_mean>=-2.367658 551 234 M (0.57531760 0.42468240)
## 4) smoothness_worst< -1.476605 301 92 M (0.69435216 0.30564784)
## 8) smoothness_worst>=-1.499656 100 10 M (0.90000000 0.10000000)
## 16) compactness_se>=-3.907039 77 1 M (0.98701299 0.01298701)
## 32) smoothness_mean< -2.221522 67 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.221522 10 1 M (0.90000000 0.10000000)
## 66) smoothness_mean>=-2.204571 9 0 M (1.00000000 0.00000000) *
## 67) smoothness_mean< -2.204571 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -3.907039 23 9 M (0.60869565 0.39130435)
## 34) smoothness_mean< -2.262441 17 3 M (0.82352941 0.17647059)
## 68) compactness_se>=-4.224388 14 0 M (1.00000000 0.00000000) *
## 69) compactness_se< -4.224388 3 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.262441 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.499656 201 82 M (0.59203980 0.40796020)
## 18) symmetry_worst< -1.543306 183 64 M (0.65027322 0.34972678)
## 36) smoothness_mean< -2.313857 81 13 M (0.83950617 0.16049383)
## 72) compactness_se< -3.492659 63 4 M (0.93650794 0.06349206) *
## 73) compactness_se>=-3.492659 18 9 M (0.50000000 0.50000000) *
## 37) smoothness_mean>=-2.313857 102 51 M (0.50000000 0.50000000)
## 74) compactness_se>=-3.685572 60 18 M (0.70000000 0.30000000) *
## 75) compactness_se< -3.685572 42 9 B (0.21428571 0.78571429) *
## 19) symmetry_worst>=-1.543306 18 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.476605 250 108 B (0.43200000 0.56800000)
## 10) smoothness_worst>=-1.473672 221 108 B (0.48868778 0.51131222)
## 20) texture_worst< 4.94309 186 82 M (0.55913978 0.44086022)
## 40) smoothness_mean< -2.306533 57 9 M (0.84210526 0.15789474)
## 80) smoothness_mean>=-2.361754 48 0 M (1.00000000 0.00000000) *
## 81) smoothness_mean< -2.361754 9 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.306533 129 56 B (0.43410853 0.56589147)
## 82) texture_worst>=4.398698 92 42 M (0.54347826 0.45652174) *
## 83) texture_worst< 4.398698 37 6 B (0.16216216 0.83783784) *
## 21) texture_worst>=4.94309 35 4 B (0.11428571 0.88571429)
## 42) compactness_se>=-3.116694 3 0 M (1.00000000 0.00000000) *
## 43) compactness_se< -3.116694 32 1 B (0.03125000 0.96875000)
## 86) texture_mean>=3.237842 1 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.237842 31 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473672 29 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.367658 361 143 B (0.39612188 0.60387812)
## 6) texture_mean< 3.336125 333 142 B (0.42642643 0.57357357)
## 12) texture_worst>=4.975502 67 23 M (0.65671642 0.34328358)
## 24) compactness_se>=-4.706178 58 14 M (0.75862069 0.24137931)
## 48) symmetry_worst>=-2.145206 46 4 M (0.91304348 0.08695652)
## 96) symmetry_worst< -1.637827 35 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst>=-1.637827 11 4 M (0.63636364 0.36363636) *
## 49) symmetry_worst< -2.145206 12 2 B (0.16666667 0.83333333)
## 98) texture_mean>=3.330945 2 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.330945 10 0 B (0.00000000 1.00000000) *
## 25) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 13) texture_worst< 4.975502 266 98 B (0.36842105 0.63157895)
## 26) smoothness_worst>=-1.604472 186 82 B (0.44086022 0.55913978)
## 52) symmetry_worst< -2.035676 22 3 M (0.86363636 0.13636364)
## 104) smoothness_worst< -1.540052 20 1 M (0.95000000 0.05000000) *
## 105) smoothness_worst>=-1.540052 2 0 B (0.00000000 1.00000000) *
## 53) symmetry_worst>=-2.035676 164 63 B (0.38414634 0.61585366)
## 106) texture_mean>=2.921008 115 54 B (0.46956522 0.53043478) *
## 107) texture_mean< 2.921008 49 9 B (0.18367347 0.81632653) *
## 27) smoothness_worst< -1.604472 80 16 B (0.20000000 0.80000000)
## 54) symmetry_worst>=-1.777195 22 10 B (0.45454545 0.54545455)
## 108) texture_mean>=2.939162 13 3 M (0.76923077 0.23076923) *
## 109) texture_mean< 2.939162 9 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.777195 58 6 B (0.10344828 0.89655172)
## 110) compactness_se>=-2.951614 10 4 M (0.60000000 0.40000000) *
## 111) compactness_se< -2.951614 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=3.336125 28 1 B (0.03571429 0.96428571)
## 14) texture_mean>=3.452615 1 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.452615 27 0 B (0.00000000 1.00000000) *
##
## $trees[[89]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 426 B (0.46710526 0.53289474)
## 2) smoothness_worst< -1.434076 775 387 M (0.50064516 0.49935484)
## 4) smoothness_mean>=-2.172878 31 0 M (1.00000000 0.00000000) *
## 5) smoothness_mean< -2.172878 744 357 B (0.47983871 0.52016129)
## 10) smoothness_worst>=-1.603315 627 302 M (0.51834131 0.48165869)
## 20) texture_worst>=4.579906 328 126 M (0.61585366 0.38414634)
## 40) texture_worst< 4.756552 150 32 M (0.78666667 0.21333333)
## 80) texture_mean>=3.055881 60 0 M (1.00000000 0.00000000) *
## 81) texture_mean< 3.055881 90 32 M (0.64444444 0.35555556) *
## 41) texture_worst>=4.756552 178 84 B (0.47191011 0.52808989)
## 82) smoothness_worst>=-1.504916 82 31 M (0.62195122 0.37804878) *
## 83) smoothness_worst< -1.504916 96 33 B (0.34375000 0.65625000) *
## 21) texture_worst< 4.579906 299 123 B (0.41137124 0.58862876)
## 42) texture_worst< 4.545141 253 118 B (0.46640316 0.53359684)
## 84) texture_worst>=4.522453 35 4 M (0.88571429 0.11428571) *
## 85) texture_worst< 4.522453 218 87 B (0.39908257 0.60091743) *
## 43) texture_worst>=4.545141 46 5 B (0.10869565 0.89130435)
## 86) texture_mean>=3.035431 5 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.035431 41 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.603315 117 32 B (0.27350427 0.72649573)
## 22) symmetry_worst>=-1.550826 14 4 M (0.71428571 0.28571429)
## 44) smoothness_mean>=-2.43698 9 0 M (1.00000000 0.00000000) *
## 45) smoothness_mean< -2.43698 5 1 B (0.20000000 0.80000000)
## 90) symmetry_worst>=-1.211778 1 0 M (1.00000000 0.00000000) *
## 91) symmetry_worst< -1.211778 4 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.550826 103 22 B (0.21359223 0.78640777)
## 46) texture_mean< 2.966301 22 10 M (0.54545455 0.45454545)
## 92) texture_mean>=2.923842 16 4 M (0.75000000 0.25000000) *
## 93) texture_mean< 2.923842 6 0 B (0.00000000 1.00000000) *
## 47) texture_mean>=2.966301 81 10 B (0.12345679 0.87654321)
## 94) smoothness_worst< -1.720903 11 5 B (0.45454545 0.54545455) *
## 95) smoothness_worst>=-1.720903 70 5 B (0.07142857 0.92857143) *
## 3) smoothness_worst>=-1.434076 137 38 B (0.27737226 0.72262774)
## 6) symmetry_worst>=-1.270655 11 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.270655 126 27 B (0.21428571 0.78571429)
## 14) smoothness_mean>=-1.977294 8 1 M (0.87500000 0.12500000)
## 28) texture_mean>=2.649801 7 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.649801 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -1.977294 118 20 B (0.16949153 0.83050847)
## 30) compactness_se>=-3.311998 12 4 M (0.66666667 0.33333333)
## 60) smoothness_mean>=-2.314128 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.314128 4 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.311998 106 12 B (0.11320755 0.88679245)
## 62) smoothness_mean< -2.305218 9 4 M (0.55555556 0.44444444)
## 124) texture_mean>=3.075523 4 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.075523 5 1 B (0.20000000 0.80000000) *
## 63) smoothness_mean>=-2.305218 97 7 B (0.07216495 0.92783505)
## 126) compactness_se< -3.475452 50 7 B (0.14000000 0.86000000) *
## 127) compactness_se>=-3.475452 47 0 B (0.00000000 1.00000000) *
##
## $trees[[90]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) smoothness_worst< -1.434076 790 389 M (0.50759494 0.49240506)
## 4) smoothness_worst>=-1.603315 662 301 M (0.54531722 0.45468278)
## 8) smoothness_mean>=-2.477713 633 277 M (0.56240126 0.43759874)
## 16) texture_mean< 2.81481 83 19 M (0.77108434 0.22891566)
## 32) compactness_se>=-3.964431 76 12 M (0.84210526 0.15789474)
## 64) compactness_se< -3.340373 67 5 M (0.92537313 0.07462687) *
## 65) compactness_se>=-3.340373 9 2 B (0.22222222 0.77777778) *
## 33) compactness_se< -3.964431 7 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=2.81481 550 258 M (0.53090909 0.46909091)
## 34) smoothness_worst< -1.59596 16 0 M (1.00000000 0.00000000) *
## 35) smoothness_worst>=-1.59596 534 258 M (0.51685393 0.48314607)
## 70) symmetry_worst>=-2.193154 475 212 M (0.55368421 0.44631579) *
## 71) symmetry_worst< -2.193154 59 13 B (0.22033898 0.77966102) *
## 9) smoothness_mean< -2.477713 29 5 B (0.17241379 0.82758621)
## 18) symmetry_worst< -2.155071 2 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst>=-2.155071 27 3 B (0.11111111 0.88888889)
## 38) texture_worst>=5.057104 7 3 B (0.42857143 0.57142857)
## 76) smoothness_worst< -1.565575 3 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst>=-1.565575 4 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 5.057104 20 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.603315 128 40 B (0.31250000 0.68750000)
## 10) smoothness_worst< -1.723213 12 1 M (0.91666667 0.08333333)
## 20) texture_mean>=3.026052 11 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.026052 1 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.723213 116 29 B (0.25000000 0.75000000)
## 22) compactness_se< -4.200032 44 21 M (0.52272727 0.47727273)
## 44) symmetry_worst>=-1.874628 31 9 M (0.70967742 0.29032258)
## 88) texture_worst< 4.998675 24 2 M (0.91666667 0.08333333) *
## 89) texture_worst>=4.998675 7 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst< -1.874628 13 1 B (0.07692308 0.92307692)
## 90) texture_mean>=3.149769 1 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 3.149769 12 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-4.200032 72 6 B (0.08333333 0.91666667)
## 46) symmetry_worst>=-1.550826 5 1 M (0.80000000 0.20000000)
## 92) smoothness_mean>=-2.49225 4 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean< -2.49225 1 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.550826 67 2 B (0.02985075 0.97014925)
## 94) texture_mean< 2.958884 10 2 B (0.20000000 0.80000000) *
## 95) texture_mean>=2.958884 57 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.434076 122 35 B (0.28688525 0.71311475)
## 6) symmetry_worst>=-1.232339 8 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.232339 114 27 B (0.23684211 0.76315789)
## 14) compactness_se>=-3.311998 12 4 M (0.66666667 0.33333333)
## 28) texture_mean>=2.701935 10 2 M (0.80000000 0.20000000)
## 56) smoothness_mean>=-2.314128 8 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.314128 2 0 B (0.00000000 1.00000000) *
## 29) texture_mean< 2.701935 2 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.311998 102 19 B (0.18627451 0.81372549)
## 30) smoothness_mean>=-1.977294 5 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -1.977294 97 14 B (0.14432990 0.85567010)
## 62) compactness_se< -3.475452 54 14 B (0.25925926 0.74074074)
## 124) symmetry_worst< -1.534985 31 14 B (0.45161290 0.54838710) *
## 125) symmetry_worst>=-1.534985 23 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-3.475452 43 0 B (0.00000000 1.00000000) *
##
## $trees[[91]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 406 B (0.44517544 0.55482456)
## 2) texture_mean< 2.787462 98 29 M (0.70408163 0.29591837)
## 4) compactness_se>=-3.891799 89 20 M (0.77528090 0.22471910)
## 8) texture_worst>=4.056844 46 1 M (0.97826087 0.02173913)
## 16) texture_mean>=2.709047 45 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.709047 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.056844 43 19 M (0.55813953 0.44186047)
## 18) smoothness_worst>=-1.498451 26 5 M (0.80769231 0.19230769)
## 36) smoothness_mean< -2.060513 22 1 M (0.95454545 0.04545455)
## 72) texture_worst>=3.768766 21 0 M (1.00000000 0.00000000) *
## 73) texture_worst< 3.768766 1 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.060513 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.498451 17 3 B (0.17647059 0.82352941)
## 38) texture_mean>=2.758692 3 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 2.758692 14 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.891799 9 0 B (0.00000000 1.00000000) *
## 3) texture_mean>=2.787462 814 337 B (0.41400491 0.58599509)
## 6) symmetry_worst< -2.49184 20 1 M (0.95000000 0.05000000)
## 12) texture_mean< 3.276838 19 0 M (1.00000000 0.00000000) *
## 13) texture_mean>=3.276838 1 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.49184 794 318 B (0.40050378 0.59949622)
## 14) symmetry_worst>=-1.327359 27 5 M (0.81481481 0.18518519)
## 28) smoothness_mean< -2.307926 18 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.307926 9 4 B (0.44444444 0.55555556)
## 58) smoothness_mean>=-2.288752 5 1 M (0.80000000 0.20000000)
## 116) compactness_se< -2.883911 4 0 M (1.00000000 0.00000000) *
## 117) compactness_se>=-2.883911 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.288752 4 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.327359 767 296 B (0.38591917 0.61408083)
## 30) smoothness_mean< -2.093138 744 295 B (0.39650538 0.60349462)
## 60) smoothness_worst>=-1.460895 141 63 M (0.55319149 0.44680851)
## 120) compactness_se>=-4.032549 72 17 M (0.76388889 0.23611111) *
## 121) compactness_se< -4.032549 69 23 B (0.33333333 0.66666667) *
## 61) smoothness_worst< -1.460895 603 217 B (0.35986733 0.64013267)
## 122) smoothness_worst< -1.4768 529 210 B (0.39697543 0.60302457) *
## 123) smoothness_worst>=-1.4768 74 7 B (0.09459459 0.90540541) *
## 31) smoothness_mean>=-2.093138 23 1 B (0.04347826 0.95652174)
## 62) texture_mean< 2.94627 1 0 M (1.00000000 0.00000000) *
## 63) texture_mean>=2.94627 22 0 B (0.00000000 1.00000000) *
##
## $trees[[92]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 417 B (0.45723684 0.54276316)
## 2) smoothness_worst< -1.532606 360 158 M (0.56111111 0.43888889)
## 4) smoothness_worst>=-1.559144 117 29 M (0.75213675 0.24786325)
## 8) smoothness_mean>=-2.48706 111 23 M (0.79279279 0.20720721)
## 16) texture_mean>=2.862952 90 10 M (0.88888889 0.11111111)
## 32) symmetry_worst>=-2.201537 80 5 M (0.93750000 0.06250000)
## 64) compactness_se>=-4.694501 78 3 M (0.96153846 0.03846154) *
## 65) compactness_se< -4.694501 2 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst< -2.201537 10 5 M (0.50000000 0.50000000)
## 66) smoothness_mean< -2.414006 5 0 M (1.00000000 0.00000000) *
## 67) smoothness_mean>=-2.414006 5 0 B (0.00000000 1.00000000) *
## 17) texture_mean< 2.862952 21 8 B (0.38095238 0.61904762)
## 34) smoothness_worst>=-1.547262 10 2 M (0.80000000 0.20000000)
## 68) compactness_se>=-4.272056 8 0 M (1.00000000 0.00000000) *
## 69) compactness_se< -4.272056 2 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst< -1.547262 11 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.48706 6 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.559144 243 114 B (0.46913580 0.53086420)
## 10) compactness_se< -4.579712 39 7 M (0.82051282 0.17948718)
## 20) compactness_se>=-4.740419 32 0 M (1.00000000 0.00000000) *
## 21) compactness_se< -4.740419 7 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.579712 204 82 B (0.40196078 0.59803922)
## 22) symmetry_worst>=-1.966444 138 66 M (0.52173913 0.47826087)
## 44) compactness_se>=-4.260936 96 34 M (0.64583333 0.35416667)
## 88) smoothness_worst< -1.568787 86 25 M (0.70930233 0.29069767) *
## 89) smoothness_worst>=-1.568787 10 1 B (0.10000000 0.90000000) *
## 45) compactness_se< -4.260936 42 10 B (0.23809524 0.76190476)
## 90) smoothness_worst< -1.61379 12 2 M (0.83333333 0.16666667) *
## 91) smoothness_worst>=-1.61379 30 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.966444 66 10 B (0.15151515 0.84848485)
## 46) smoothness_worst< -1.694089 10 4 M (0.60000000 0.40000000)
## 92) texture_mean>=3.03091 7 1 M (0.85714286 0.14285714) *
## 93) texture_mean< 3.03091 3 0 B (0.00000000 1.00000000) *
## 47) smoothness_worst>=-1.694089 56 4 B (0.07142857 0.92857143)
## 94) compactness_se>=-2.674921 2 0 M (1.00000000 0.00000000) *
## 95) compactness_se< -2.674921 54 2 B (0.03703704 0.96296296) *
## 3) smoothness_worst>=-1.532606 552 215 B (0.38949275 0.61050725)
## 6) smoothness_mean< -2.235394 433 190 B (0.43879908 0.56120092)
## 12) texture_worst< 4.545516 132 47 M (0.64393939 0.35606061)
## 24) smoothness_worst>=-1.520499 119 34 M (0.71428571 0.28571429)
## 48) texture_mean>=2.892314 44 1 M (0.97727273 0.02272727)
## 96) compactness_se>=-3.950529 39 0 M (1.00000000 0.00000000) *
## 97) compactness_se< -3.950529 5 1 M (0.80000000 0.20000000) *
## 49) texture_mean< 2.892314 75 33 M (0.56000000 0.44000000)
## 98) smoothness_worst>=-1.496838 63 21 M (0.66666667 0.33333333) *
## 99) smoothness_worst< -1.496838 12 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.520499 13 0 B (0.00000000 1.00000000) *
## 13) texture_worst>=4.545516 301 105 B (0.34883721 0.65116279)
## 26) texture_mean>=2.975018 245 99 B (0.40408163 0.59591837)
## 52) compactness_se< -3.500605 155 74 M (0.52258065 0.47741935)
## 104) texture_mean< 3.210432 102 30 M (0.70588235 0.29411765) *
## 105) texture_mean>=3.210432 53 9 B (0.16981132 0.83018868) *
## 53) compactness_se>=-3.500605 90 18 B (0.20000000 0.80000000)
## 106) smoothness_mean>=-2.281841 8 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.281841 82 10 B (0.12195122 0.87804878) *
## 27) texture_mean< 2.975018 56 6 B (0.10714286 0.89285714)
## 54) symmetry_worst>=-1.420115 3 0 M (1.00000000 0.00000000) *
## 55) symmetry_worst< -1.420115 53 3 B (0.05660377 0.94339623)
## 110) texture_mean< 2.851854 2 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.851854 51 1 B (0.01960784 0.98039216) *
## 7) smoothness_mean>=-2.235394 119 25 B (0.21008403 0.78991597)
## 14) smoothness_mean>=-2.07745 8 1 M (0.87500000 0.12500000)
## 28) symmetry_worst< -1.400188 7 0 M (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.400188 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.07745 111 18 B (0.16216216 0.83783784)
## 30) texture_mean>=3.044522 20 10 M (0.50000000 0.50000000)
## 60) texture_mean< 3.184212 10 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.184212 10 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.044522 91 8 B (0.08791209 0.91208791)
## 62) compactness_se< -3.492408 41 8 B (0.19512195 0.80487805)
## 124) compactness_se>=-3.664511 3 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -3.664511 38 5 B (0.13157895 0.86842105) *
## 63) compactness_se>=-3.492408 50 0 B (0.00000000 1.00000000) *
##
## $trees[[93]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 B (0.48245614 0.51754386)
## 2) smoothness_worst< -1.4768 618 282 M (0.54368932 0.45631068)
## 4) smoothness_worst>=-1.604472 521 210 M (0.59692898 0.40307102)
## 8) smoothness_worst>=-1.482699 51 6 M (0.88235294 0.11764706)
## 16) compactness_se>=-3.894783 43 0 M (1.00000000 0.00000000) *
## 17) compactness_se< -3.894783 8 2 B (0.25000000 0.75000000)
## 34) smoothness_mean< -2.282367 2 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean>=-2.282367 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482699 470 204 M (0.56595745 0.43404255)
## 18) smoothness_worst< -1.484675 455 189 M (0.58461538 0.41538462)
## 36) texture_mean>=3.034949 171 51 M (0.70175439 0.29824561)
## 72) texture_worst< 4.797934 43 0 M (1.00000000 0.00000000) *
## 73) texture_worst>=4.797934 128 51 M (0.60156250 0.39843750) *
## 37) texture_mean< 3.034949 284 138 M (0.51408451 0.48591549)
## 74) symmetry_worst< -2.111279 36 5 M (0.86111111 0.13888889) *
## 75) symmetry_worst>=-2.111279 248 115 B (0.46370968 0.53629032) *
## 19) smoothness_worst>=-1.484675 15 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.604472 97 25 B (0.25773196 0.74226804)
## 10) symmetry_worst>=-1.550826 12 3 M (0.75000000 0.25000000)
## 20) texture_mean>=2.967432 10 1 M (0.90000000 0.10000000)
## 40) smoothness_mean>=-2.592204 9 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean< -2.592204 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.967432 2 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.550826 85 16 B (0.18823529 0.81176471)
## 22) texture_mean< 3.086027 41 16 B (0.39024390 0.60975610)
## 44) texture_mean>=2.935975 27 11 M (0.59259259 0.40740741)
## 88) smoothness_mean< -2.484925 23 7 M (0.69565217 0.30434783) *
## 89) smoothness_mean>=-2.484925 4 0 B (0.00000000 1.00000000) *
## 45) texture_mean< 2.935975 14 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=3.086027 44 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.4768 294 104 B (0.35374150 0.64625850)
## 6) symmetry_worst>=-1.352813 27 5 M (0.81481481 0.18518519)
## 12) smoothness_mean>=-2.365259 24 2 M (0.91666667 0.08333333)
## 24) smoothness_mean< -2.003731 22 1 M (0.95454545 0.04545455)
## 48) compactness_se< -2.646661 20 0 M (1.00000000 0.00000000) *
## 49) compactness_se>=-2.646661 2 1 M (0.50000000 0.50000000)
## 98) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.915767 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.003731 2 1 M (0.50000000 0.50000000)
## 50) texture_mean>=2.823221 1 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.823221 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.365259 3 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.352813 267 82 B (0.30711610 0.69288390)
## 14) texture_worst< 4.624204 97 47 B (0.48453608 0.51546392)
## 28) texture_mean>=2.934384 36 2 M (0.94444444 0.05555556)
## 56) compactness_se>=-4.35833 34 0 M (1.00000000 0.00000000) *
## 57) compactness_se< -4.35833 2 0 B (0.00000000 1.00000000) *
## 29) texture_mean< 2.934384 61 13 B (0.21311475 0.78688525)
## 58) texture_mean< 2.754252 13 4 M (0.69230769 0.30769231)
## 116) texture_mean>=2.728421 7 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.728421 6 2 B (0.33333333 0.66666667) *
## 59) texture_mean>=2.754252 48 4 B (0.08333333 0.91666667)
## 118) smoothness_worst>=-1.449274 14 4 B (0.28571429 0.71428571) *
## 119) smoothness_worst< -1.449274 34 0 B (0.00000000 1.00000000) *
## 15) texture_worst>=4.624204 170 35 B (0.20588235 0.79411765)
## 30) texture_worst>=4.824912 83 33 B (0.39759036 0.60240964)
## 60) symmetry_worst< -1.820896 11 0 M (1.00000000 0.00000000) *
## 61) symmetry_worst>=-1.820896 72 22 B (0.30555556 0.69444444)
## 122) symmetry_worst>=-1.655812 32 11 M (0.65625000 0.34375000) *
## 123) symmetry_worst< -1.655812 40 1 B (0.02500000 0.97500000) *
## 31) texture_worst< 4.824912 87 2 B (0.02298851 0.97701149)
## 62) compactness_se< -4.222024 1 0 M (1.00000000 0.00000000) *
## 63) compactness_se>=-4.222024 86 1 B (0.01162791 0.98837209)
## 126) compactness_se>=-3.370923 1 0 M (1.00000000 0.00000000) *
## 127) compactness_se< -3.370923 85 0 B (0.00000000 1.00000000) *
##
## $trees[[94]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) texture_worst< 4.642157 458 195 M (0.57423581 0.42576419)
## 4) compactness_se< -3.344528 362 133 M (0.63259669 0.36740331)
## 8) symmetry_worst< -1.692017 264 75 M (0.71590909 0.28409091)
## 16) symmetry_worst>=-1.815934 114 11 M (0.90350877 0.09649123)
## 32) compactness_se>=-4.45131 112 9 M (0.91964286 0.08035714)
## 64) smoothness_mean< -2.188699 111 8 M (0.92792793 0.07207207) *
## 65) smoothness_mean>=-2.188699 1 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.45131 2 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.815934 150 64 M (0.57333333 0.42666667)
## 34) smoothness_mean>=-2.419122 121 40 M (0.66942149 0.33057851)
## 68) symmetry_worst< -1.824299 109 29 M (0.73394495 0.26605505) *
## 69) symmetry_worst>=-1.824299 12 1 B (0.08333333 0.91666667) *
## 35) smoothness_mean< -2.419122 29 5 B (0.17241379 0.82758621)
## 70) compactness_se>=-3.514597 10 5 M (0.50000000 0.50000000) *
## 71) compactness_se< -3.514597 19 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.692017 98 40 B (0.40816327 0.59183673)
## 18) smoothness_worst>=-1.451541 19 3 M (0.84210526 0.15789474)
## 36) smoothness_worst< -1.42057 13 0 M (1.00000000 0.00000000) *
## 37) smoothness_worst>=-1.42057 6 3 M (0.50000000 0.50000000)
## 74) texture_mean>=2.688296 3 0 M (1.00000000 0.00000000) *
## 75) texture_mean< 2.688296 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.451541 79 24 B (0.30379747 0.69620253)
## 38) compactness_se< -4.681232 14 3 M (0.78571429 0.21428571)
## 76) texture_mean< 2.936149 11 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=2.936149 3 0 B (0.00000000 1.00000000) *
## 39) compactness_se>=-4.681232 65 13 B (0.20000000 0.80000000)
## 78) smoothness_mean>=-2.170258 6 0 M (1.00000000 0.00000000) *
## 79) smoothness_mean< -2.170258 59 7 B (0.11864407 0.88135593) *
## 5) compactness_se>=-3.344528 96 34 B (0.35416667 0.64583333)
## 10) symmetry_worst>=-1.001713 16 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.001713 80 18 B (0.22500000 0.77500000)
## 22) texture_mean>=3.023554 9 1 M (0.88888889 0.11111111)
## 44) texture_worst< 4.544 8 0 M (1.00000000 0.00000000) *
## 45) texture_worst>=4.544 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.023554 71 10 B (0.14084507 0.85915493)
## 46) smoothness_worst>=-1.500893 29 10 B (0.34482759 0.65517241)
## 92) texture_mean>=2.915992 9 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.915992 20 1 B (0.05000000 0.95000000) *
## 47) smoothness_worst< -1.500893 42 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.642157 454 170 B (0.37444934 0.62555066)
## 6) compactness_se>=-3.334337 80 29 M (0.63750000 0.36250000)
## 12) smoothness_mean>=-2.338127 34 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.338127 46 17 B (0.36956522 0.63043478)
## 26) texture_worst>=4.993407 16 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.993407 30 1 B (0.03333333 0.96666667)
## 54) texture_worst< 4.684099 4 1 B (0.25000000 0.75000000)
## 108) texture_mean>=3.079461 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 3.079461 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.684099 26 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.334337 374 119 B (0.31818182 0.68181818)
## 14) smoothness_mean>=-2.403622 255 98 B (0.38431373 0.61568627)
## 28) smoothness_mean< -2.382712 33 3 M (0.90909091 0.09090909)
## 56) symmetry_worst>=-2.212871 31 1 M (0.96774194 0.03225806)
## 112) texture_mean>=2.920077 30 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.920077 1 0 B (0.00000000 1.00000000) *
## 57) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.382712 222 68 B (0.30630631 0.69369369)
## 58) texture_worst>=4.818867 126 53 B (0.42063492 0.57936508)
## 116) symmetry_worst>=-1.71268 40 11 M (0.72500000 0.27500000) *
## 117) symmetry_worst< -1.71268 86 24 B (0.27906977 0.72093023) *
## 59) texture_worst< 4.818867 96 15 B (0.15625000 0.84375000)
## 118) smoothness_mean< -2.353585 9 2 M (0.77777778 0.22222222) *
## 119) smoothness_mean>=-2.353585 87 8 B (0.09195402 0.90804598) *
## 15) smoothness_mean< -2.403622 119 21 B (0.17647059 0.82352941)
## 30) smoothness_mean< -2.443746 66 21 B (0.31818182 0.68181818)
## 60) smoothness_mean>=-2.450864 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.450864 58 13 B (0.22413793 0.77586207)
## 122) compactness_se< -4.620161 13 5 M (0.61538462 0.38461538) *
## 123) compactness_se>=-4.620161 45 5 B (0.11111111 0.88888889) *
## 31) smoothness_mean>=-2.443746 53 0 B (0.00000000 1.00000000) *
##
## $trees[[95]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) smoothness_worst>=-1.609811 812 389 M (0.52093596 0.47906404)
## 4) smoothness_worst< -1.533657 271 99 M (0.63468635 0.36531365)
## 8) compactness_se>=-3.545992 67 9 M (0.86567164 0.13432836)
## 16) texture_worst>=4.411908 50 2 M (0.96000000 0.04000000)
## 32) symmetry_worst>=-1.956813 41 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -1.956813 9 2 M (0.77777778 0.22222222)
## 66) texture_mean>=3.044039 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.044039 2 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.411908 17 7 M (0.58823529 0.41176471)
## 34) compactness_se< -3.464112 10 0 M (1.00000000 0.00000000) *
## 35) compactness_se>=-3.464112 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.545992 204 90 M (0.55882353 0.44117647)
## 18) smoothness_mean>=-2.501158 193 79 M (0.59067358 0.40932642)
## 36) symmetry_worst>=-2.063958 159 55 M (0.65408805 0.34591195)
## 72) texture_mean>=2.983598 74 8 M (0.89189189 0.10810811) *
## 73) texture_mean< 2.983598 85 38 B (0.44705882 0.55294118) *
## 37) symmetry_worst< -2.063958 34 10 B (0.29411765 0.70588235)
## 74) smoothness_worst< -1.594361 10 0 M (1.00000000 0.00000000) *
## 75) smoothness_worst>=-1.594361 24 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.501158 11 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.533657 541 251 B (0.46395564 0.53604436)
## 10) smoothness_worst>=-1.52382 490 244 M (0.50204082 0.49795918)
## 20) compactness_se>=-4.547852 463 217 M (0.53131749 0.46868251)
## 40) symmetry_worst>=-1.529476 85 20 M (0.76470588 0.23529412)
## 80) smoothness_mean>=-2.343303 75 10 M (0.86666667 0.13333333) *
## 81) smoothness_mean< -2.343303 10 0 B (0.00000000 1.00000000) *
## 41) symmetry_worst< -1.529476 378 181 B (0.47883598 0.52116402)
## 82) smoothness_mean< -2.299091 187 73 M (0.60962567 0.39037433) *
## 83) smoothness_mean>=-2.299091 191 67 B (0.35078534 0.64921466) *
## 21) compactness_se< -4.547852 27 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.52382 51 5 B (0.09803922 0.90196078)
## 22) texture_mean>=3.084198 13 5 B (0.38461538 0.61538462)
## 44) texture_worst< 4.870528 5 0 M (1.00000000 0.00000000) *
## 45) texture_worst>=4.870528 8 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.084198 38 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.609811 100 25 B (0.25000000 0.75000000)
## 6) symmetry_worst>=-1.550826 8 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.550826 92 17 B (0.18478261 0.81521739)
## 14) texture_mean< 3.086027 58 17 B (0.29310345 0.70689655)
## 28) texture_worst>=4.818554 6 0 M (1.00000000 0.00000000) *
## 29) texture_worst< 4.818554 52 11 B (0.21153846 0.78846154)
## 58) symmetry_worst>=-1.627715 5 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.627715 47 6 B (0.12765957 0.87234043)
## 118) texture_mean>=3.078218 4 0 M (1.00000000 0.00000000) *
## 119) texture_mean< 3.078218 43 2 B (0.04651163 0.95348837) *
## 15) texture_mean>=3.086027 34 0 B (0.00000000 1.00000000) *
##
## $trees[[96]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.51096491 0.48903509)
## 2) texture_worst< 4.782287 617 268 M (0.56564019 0.43435981)
## 4) symmetry_worst>=-1.834844 434 145 M (0.66589862 0.33410138)
## 8) symmetry_worst< -1.69453 216 48 M (0.77777778 0.22222222)
## 16) smoothness_worst>=-1.587787 194 33 M (0.82989691 0.17010309)
## 32) texture_mean>=2.891759 106 5 M (0.95283019 0.04716981)
## 64) smoothness_mean>=-2.450833 103 2 M (0.98058252 0.01941748) *
## 65) smoothness_mean< -2.450833 3 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.891759 88 28 M (0.68181818 0.31818182)
## 66) symmetry_worst< -1.801798 38 0 M (1.00000000 0.00000000) *
## 67) symmetry_worst>=-1.801798 50 22 B (0.44000000 0.56000000) *
## 17) smoothness_worst< -1.587787 22 7 B (0.31818182 0.68181818)
## 34) symmetry_worst>=-1.787851 9 2 M (0.77777778 0.22222222)
## 68) texture_mean>=2.935975 7 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.935975 2 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst< -1.787851 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.69453 218 97 M (0.55504587 0.44495413)
## 18) symmetry_worst>=-1.656986 203 82 M (0.59605911 0.40394089)
## 36) texture_mean>=3.061712 40 6 M (0.85000000 0.15000000)
## 72) smoothness_worst>=-1.609426 34 0 M (1.00000000 0.00000000) *
## 73) smoothness_worst< -1.609426 6 0 B (0.00000000 1.00000000) *
## 37) texture_mean< 3.061712 163 76 M (0.53374233 0.46625767)
## 74) texture_mean< 3.04903 149 62 M (0.58389262 0.41610738) *
## 75) texture_mean>=3.04903 14 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.656986 15 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.834844 183 60 B (0.32786885 0.67213115)
## 10) smoothness_worst< -1.542557 65 29 M (0.55384615 0.44615385)
## 20) smoothness_worst>=-1.559148 19 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.559148 46 17 B (0.36956522 0.63043478)
## 42) compactness_se>=-3.604909 14 4 M (0.71428571 0.28571429)
## 84) symmetry_worst< -1.953067 12 2 M (0.83333333 0.16666667) *
## 85) symmetry_worst>=-1.953067 2 0 B (0.00000000 1.00000000) *
## 43) compactness_se< -3.604909 32 7 B (0.21875000 0.78125000)
## 86) compactness_se< -4.563271 5 1 M (0.80000000 0.20000000) *
## 87) compactness_se>=-4.563271 27 3 B (0.11111111 0.88888889) *
## 11) smoothness_worst>=-1.542557 118 24 B (0.20338983 0.79661017)
## 22) smoothness_worst>=-1.497846 54 22 B (0.40740741 0.59259259)
## 44) smoothness_worst< -1.476215 20 4 M (0.80000000 0.20000000)
## 88) compactness_se>=-3.79429 14 0 M (1.00000000 0.00000000) *
## 89) compactness_se< -3.79429 6 2 B (0.33333333 0.66666667) *
## 45) smoothness_worst>=-1.476215 34 6 B (0.17647059 0.82352941)
## 90) smoothness_worst>=-1.424105 7 2 M (0.71428571 0.28571429) *
## 91) smoothness_worst< -1.424105 27 1 B (0.03703704 0.96296296) *
## 23) smoothness_worst< -1.497846 64 2 B (0.03125000 0.96875000)
## 46) texture_worst>=4.649493 2 0 M (1.00000000 0.00000000) *
## 47) texture_worst< 4.649493 62 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.782287 295 117 B (0.39661017 0.60338983)
## 6) texture_worst>=4.911888 199 98 M (0.50753769 0.49246231)
## 12) symmetry_worst< -1.733593 104 27 M (0.74038462 0.25961538)
## 24) symmetry_worst>=-2.207988 85 11 M (0.87058824 0.12941176)
## 48) compactness_se>=-4.706178 81 7 M (0.91358025 0.08641975)
## 96) smoothness_mean< -2.140427 79 5 M (0.93670886 0.06329114) *
## 97) smoothness_mean>=-2.140427 2 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.706178 4 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -2.207988 19 3 B (0.15789474 0.84210526)
## 50) compactness_se>=-3.413706 3 0 M (1.00000000 0.00000000) *
## 51) compactness_se< -3.413706 16 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.733593 95 24 B (0.25263158 0.74736842)
## 26) smoothness_worst>=-1.426681 11 0 M (1.00000000 0.00000000) *
## 27) smoothness_worst< -1.426681 84 13 B (0.15476190 0.84523810)
## 54) texture_worst< 4.941163 3 0 M (1.00000000 0.00000000) *
## 55) texture_worst>=4.941163 81 10 B (0.12345679 0.87654321)
## 110) texture_worst>=5.003123 38 10 B (0.26315789 0.73684211) *
## 111) texture_worst< 5.003123 43 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.911888 96 16 B (0.16666667 0.83333333)
## 14) compactness_se>=-3.601962 25 12 B (0.48000000 0.52000000)
## 28) smoothness_worst< -1.506135 11 1 M (0.90909091 0.09090909)
## 56) smoothness_mean>=-2.51419 10 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.51419 1 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst>=-1.506135 14 2 B (0.14285714 0.85714286)
## 58) smoothness_mean>=-2.272702 1 0 M (1.00000000 0.00000000) *
## 59) smoothness_mean< -2.272702 13 1 B (0.07692308 0.92307692)
## 118) compactness_se< -3.500605 1 0 M (1.00000000 0.00000000) *
## 119) compactness_se>=-3.500605 12 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.601962 71 4 B (0.05633803 0.94366197)
## 30) symmetry_worst>=-1.35602 3 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst< -1.35602 68 1 B (0.01470588 0.98529412)
## 62) smoothness_mean< -2.480592 4 1 B (0.25000000 0.75000000)
## 124) texture_mean< 3.136036 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean>=3.136036 3 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.480592 64 0 B (0.00000000 1.00000000) *
##
## $trees[[97]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) symmetry_worst>=-2.031981 769 371 B (0.48244473 0.51755527)
## 4) smoothness_worst< -1.4768 498 227 M (0.54417671 0.45582329)
## 8) smoothness_worst>=-1.482699 42 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.482699 456 227 M (0.50219298 0.49780702)
## 18) smoothness_worst>=-1.584838 362 161 M (0.55524862 0.44475138)
## 36) symmetry_worst< -1.730674 175 56 M (0.68000000 0.32000000)
## 72) compactness_se< -3.93685 92 15 M (0.83695652 0.16304348) *
## 73) compactness_se>=-3.93685 83 41 M (0.50602410 0.49397590) *
## 37) symmetry_worst>=-1.730674 187 82 B (0.43850267 0.56149733)
## 74) compactness_se>=-3.681558 83 28 M (0.66265060 0.33734940) *
## 75) compactness_se< -3.681558 104 27 B (0.25961538 0.74038462) *
## 19) smoothness_worst< -1.584838 94 28 B (0.29787234 0.70212766)
## 38) smoothness_mean< -2.473852 52 25 B (0.48076923 0.51923077)
## 76) smoothness_mean>=-2.507153 26 5 M (0.80769231 0.19230769) *
## 77) smoothness_mean< -2.507153 26 4 B (0.15384615 0.84615385) *
## 39) smoothness_mean>=-2.473852 42 3 B (0.07142857 0.92857143)
## 78) symmetry_worst>=-1.550826 4 1 M (0.75000000 0.25000000) *
## 79) symmetry_worst< -1.550826 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.4768 271 100 B (0.36900369 0.63099631)
## 10) smoothness_worst>=-1.472112 220 99 B (0.45000000 0.55000000)
## 20) symmetry_worst>=-1.75757 181 88 M (0.51381215 0.48618785)
## 40) compactness_se>=-4.032549 108 36 M (0.66666667 0.33333333)
## 80) texture_worst>=4.40818 62 7 M (0.88709677 0.11290323) *
## 81) texture_worst< 4.40818 46 17 B (0.36956522 0.63043478) *
## 41) compactness_se< -4.032549 73 21 B (0.28767123 0.71232877)
## 82) symmetry_worst< -1.743442 9 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst>=-1.743442 64 12 B (0.18750000 0.81250000) *
## 21) symmetry_worst< -1.75757 39 6 B (0.15384615 0.84615385)
## 42) texture_worst>=5.041355 3 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.041355 36 3 B (0.08333333 0.91666667)
## 86) compactness_se< -3.791636 5 2 B (0.40000000 0.60000000) *
## 87) compactness_se>=-3.791636 31 1 B (0.03225806 0.96774194) *
## 11) smoothness_worst< -1.472112 51 1 B (0.01960784 0.98039216)
## 22) texture_mean>=3.069079 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.069079 50 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.031981 143 39 B (0.27272727 0.72727273)
## 6) symmetry_worst< -2.813177 9 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst>=-2.813177 134 30 B (0.22388060 0.77611940)
## 14) smoothness_worst< -1.720903 6 2 M (0.66666667 0.33333333)
## 28) texture_mean< 3.103494 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.103494 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.720903 128 26 B (0.20312500 0.79687500)
## 30) smoothness_worst>=-1.448989 2 0 M (1.00000000 0.00000000) *
## 31) smoothness_worst< -1.448989 126 24 B (0.19047619 0.80952381)
## 62) symmetry_worst< -2.384404 10 5 M (0.50000000 0.50000000)
## 124) symmetry_worst>=-2.522371 5 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst< -2.522371 5 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-2.384404 116 19 B (0.16379310 0.83620690)
## 126) smoothness_mean< -2.334592 72 18 B (0.25000000 0.75000000) *
## 127) smoothness_mean>=-2.334592 44 1 B (0.02272727 0.97727273) *
##
## $trees[[98]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) compactness_se< -3.132769 817 408 M (0.50061200 0.49938800)
## 4) symmetry_worst< -1.503393 719 338 M (0.52990264 0.47009736)
## 8) compactness_se>=-3.439211 78 17 M (0.78205128 0.21794872)
## 16) smoothness_mean< -2.231223 69 9 M (0.86956522 0.13043478)
## 32) texture_worst>=4.40102 48 2 M (0.95833333 0.04166667)
## 64) smoothness_worst>=-1.579519 44 0 M (1.00000000 0.00000000) *
## 65) smoothness_worst< -1.579519 4 2 M (0.50000000 0.50000000) *
## 33) texture_worst< 4.40102 21 7 M (0.66666667 0.33333333)
## 66) compactness_se< -3.420409 14 0 M (1.00000000 0.00000000) *
## 67) compactness_se>=-3.420409 7 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.231223 9 1 B (0.11111111 0.88888889)
## 34) texture_mean>=3.004098 2 1 M (0.50000000 0.50000000)
## 68) texture_mean< 3.140897 1 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.140897 1 0 B (0.00000000 1.00000000) *
## 35) texture_mean< 3.004098 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.439211 641 320 B (0.49921997 0.50078003)
## 18) compactness_se< -3.492659 563 257 M (0.54351687 0.45648313)
## 36) smoothness_worst>=-1.424105 29 1 M (0.96551724 0.03448276)
## 72) compactness_se>=-4.089202 28 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.089202 1 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst< -1.424105 534 256 M (0.52059925 0.47940075)
## 74) texture_mean< 2.84432 95 24 M (0.74736842 0.25263158) *
## 75) texture_mean>=2.84432 439 207 B (0.47152620 0.52847380) *
## 19) compactness_se>=-3.492659 78 14 B (0.17948718 0.82051282)
## 38) smoothness_worst< -1.542472 15 5 M (0.66666667 0.33333333)
## 76) smoothness_worst>=-1.618016 10 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst< -1.618016 5 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.542472 63 4 B (0.06349206 0.93650794)
## 78) texture_mean>=3.061712 5 2 M (0.60000000 0.40000000) *
## 79) texture_mean< 3.061712 58 1 B (0.01724138 0.98275862) *
## 5) symmetry_worst>=-1.503393 98 28 B (0.28571429 0.71428571)
## 10) symmetry_worst>=-1.322543 25 8 M (0.68000000 0.32000000)
## 20) texture_mean>=2.763873 21 4 M (0.80952381 0.19047619)
## 40) smoothness_worst>=-1.496291 16 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.496291 5 1 B (0.20000000 0.80000000)
## 82) texture_mean>=3.158816 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.158816 4 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.763873 4 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.322543 73 11 B (0.15068493 0.84931507)
## 22) smoothness_mean< -2.370743 20 9 B (0.45000000 0.55000000)
## 44) smoothness_mean>=-2.425324 7 0 M (1.00000000 0.00000000) *
## 45) smoothness_mean< -2.425324 13 2 B (0.15384615 0.84615385)
## 90) texture_mean>=2.97943 2 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.97943 11 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.370743 53 2 B (0.03773585 0.96226415)
## 46) compactness_se>=-3.807621 12 2 B (0.16666667 0.83333333)
## 92) compactness_se< -3.597317 2 0 M (1.00000000 0.00000000) *
## 93) compactness_se>=-3.597317 10 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.807621 41 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-3.132769 95 24 B (0.25263158 0.74736842)
## 6) smoothness_mean>=-2.291354 39 17 M (0.56410256 0.43589744)
## 12) smoothness_worst>=-1.502935 29 7 M (0.75862069 0.24137931)
## 24) compactness_se< -2.552001 21 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-2.552001 8 1 B (0.12500000 0.87500000)
## 50) texture_mean>=2.929061 1 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.929061 7 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.502935 10 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.291354 56 2 B (0.03571429 0.96428571)
## 14) smoothness_worst< -1.720903 2 1 M (0.50000000 0.50000000)
## 28) texture_mean< 3.103494 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.720903 54 1 B (0.01851852 0.98148148)
## 30) texture_mean>=3.083423 15 1 B (0.06666667 0.93333333)
## 60) texture_mean< 3.109209 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.109209 14 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.083423 39 0 B (0.00000000 1.00000000) *
##
## $trees[[99]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 M (0.52850877 0.47149123)
## 2) texture_worst>=4.580648 508 204 M (0.59842520 0.40157480)
## 4) texture_worst< 4.642157 98 12 M (0.87755102 0.12244898)
## 8) smoothness_worst>=-1.594449 91 6 M (0.93406593 0.06593407)
## 16) compactness_se>=-4.694501 88 3 M (0.96590909 0.03409091)
## 32) symmetry_worst>=-2.477165 86 1 M (0.98837209 0.01162791)
## 64) smoothness_mean< -2.227061 65 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.227061 21 1 M (0.95238095 0.04761905) *
## 33) symmetry_worst< -2.477165 2 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.594449 7 1 B (0.14285714 0.85714286)
## 18) smoothness_mean< -2.558761 1 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.558761 6 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.642157 410 192 M (0.53170732 0.46829268)
## 10) texture_worst>=4.681966 369 155 M (0.57994580 0.42005420)
## 20) smoothness_worst< -1.452126 292 107 M (0.63356164 0.36643836)
## 40) smoothness_worst>=-1.50249 100 16 M (0.84000000 0.16000000)
## 80) smoothness_mean>=-2.339781 64 3 M (0.95312500 0.04687500) *
## 81) smoothness_mean< -2.339781 36 13 M (0.63888889 0.36111111) *
## 41) smoothness_worst< -1.50249 192 91 M (0.52604167 0.47395833)
## 82) symmetry_worst< -2.121358 28 3 M (0.89285714 0.10714286) *
## 83) symmetry_worst>=-2.121358 164 76 B (0.46341463 0.53658537) *
## 21) smoothness_worst>=-1.452126 77 29 B (0.37662338 0.62337662)
## 42) compactness_se>=-4.032549 33 12 M (0.63636364 0.36363636)
## 84) compactness_se< -3.425387 24 4 M (0.83333333 0.16666667) *
## 85) compactness_se>=-3.425387 9 1 B (0.11111111 0.88888889) *
## 43) compactness_se< -4.032549 44 8 B (0.18181818 0.81818182)
## 86) smoothness_worst>=-1.425207 5 1 M (0.80000000 0.20000000) *
## 87) smoothness_worst< -1.425207 39 4 B (0.10256410 0.89743590) *
## 11) texture_worst< 4.681966 41 4 B (0.09756098 0.90243902)
## 22) texture_mean>=3.067341 4 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.067341 37 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.580648 404 178 B (0.44059406 0.55940594)
## 6) texture_worst< 4.543638 341 169 M (0.50439883 0.49560117)
## 12) smoothness_mean< -2.411844 86 23 M (0.73255814 0.26744186)
## 24) compactness_se< -3.483667 75 12 M (0.84000000 0.16000000)
## 48) symmetry_worst>=-2.079923 71 8 M (0.88732394 0.11267606)
## 96) texture_mean>=2.745392 68 5 M (0.92647059 0.07352941) *
## 97) texture_mean< 2.745392 3 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst< -2.079923 4 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.483667 11 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.411844 255 109 B (0.42745098 0.57254902)
## 26) compactness_se>=-3.891799 149 62 M (0.58389262 0.41610738)
## 52) smoothness_worst>=-1.503711 93 27 M (0.70967742 0.29032258)
## 104) smoothness_mean< -2.271585 29 0 M (1.00000000 0.00000000) *
## 105) smoothness_mean>=-2.271585 64 27 M (0.57812500 0.42187500) *
## 53) smoothness_worst< -1.503711 56 21 B (0.37500000 0.62500000)
## 106) smoothness_worst< -1.565495 19 4 M (0.78947368 0.21052632) *
## 107) smoothness_worst>=-1.565495 37 6 B (0.16216216 0.83783784) *
## 27) compactness_se< -3.891799 106 22 B (0.20754717 0.79245283)
## 54) texture_worst>=4.531936 9 0 M (1.00000000 0.00000000) *
## 55) texture_worst< 4.531936 97 13 B (0.13402062 0.86597938)
## 110) symmetry_worst< -2.49184 4 0 M (1.00000000 0.00000000) *
## 111) symmetry_worst>=-2.49184 93 9 B (0.09677419 0.90322581) *
## 7) texture_worst>=4.543638 63 6 B (0.09523810 0.90476190)
## 14) texture_mean>=3.07959 3 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.07959 60 3 B (0.05000000 0.95000000)
## 30) smoothness_mean< -2.486703 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=2.935975 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.935975 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.486703 57 1 B (0.01754386 0.98245614)
## 62) compactness_se>=-3.096414 1 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -3.096414 56 0 B (0.00000000 1.00000000) *
##
## $trees[[100]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 412 M (0.54824561 0.45175439)
## 2) texture_worst>=4.824912 274 84 M (0.69343066 0.30656934)
## 4) smoothness_worst>=-1.623453 260 71 M (0.72692308 0.27307692)
## 8) symmetry_worst>=-2.207988 236 53 M (0.77542373 0.22457627)
## 16) symmetry_worst< -1.733593 115 10 M (0.91304348 0.08695652)
## 32) texture_worst>=4.897936 109 5 M (0.95412844 0.04587156)
## 64) compactness_se>=-4.706178 106 2 M (0.98113208 0.01886792) *
## 65) compactness_se< -4.706178 3 0 B (0.00000000 1.00000000) *
## 33) texture_worst< 4.897936 6 1 B (0.16666667 0.83333333)
## 66) texture_mean< 3.156152 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.156152 5 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.733593 121 43 M (0.64462810 0.35537190)
## 34) symmetry_worst>=-1.703871 98 25 M (0.74489796 0.25510204)
## 68) compactness_se>=-4.418276 73 10 M (0.86301370 0.13698630) *
## 69) compactness_se< -4.418276 25 10 B (0.40000000 0.60000000) *
## 35) symmetry_worst< -1.703871 23 5 B (0.21739130 0.78260870)
## 70) texture_mean< 2.982883 5 0 M (1.00000000 0.00000000) *
## 71) texture_mean>=2.982883 18 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.207988 24 6 B (0.25000000 0.75000000)
## 18) compactness_se>=-3.413706 6 0 M (1.00000000 0.00000000) *
## 19) compactness_se< -3.413706 18 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.623453 14 1 B (0.07142857 0.92857143)
## 10) smoothness_mean>=-2.396135 1 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean< -2.396135 13 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.824912 638 310 B (0.48589342 0.51410658)
## 6) texture_worst< 4.747573 576 276 M (0.52083333 0.47916667)
## 12) texture_mean>=3.055881 72 8 M (0.88888889 0.11111111)
## 24) smoothness_worst>=-1.606352 52 0 M (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.606352 20 8 M (0.60000000 0.40000000)
## 50) smoothness_worst< -1.678162 13 1 M (0.92307692 0.07692308)
## 100) texture_worst< 4.575818 12 0 M (1.00000000 0.00000000) *
## 101) texture_worst>=4.575818 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.678162 7 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 3.055881 504 236 B (0.46825397 0.53174603)
## 26) compactness_se< -3.492332 348 155 M (0.55459770 0.44540230)
## 52) symmetry_worst< -1.503393 312 122 M (0.60897436 0.39102564)
## 104) smoothness_worst>=-1.451352 56 7 M (0.87500000 0.12500000) *
## 105) smoothness_worst< -1.451352 256 115 M (0.55078125 0.44921875) *
## 53) symmetry_worst>=-1.503393 36 3 B (0.08333333 0.91666667)
## 106) texture_mean>=2.998678 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean< 2.998678 35 2 B (0.05714286 0.94285714) *
## 27) compactness_se>=-3.492332 156 43 B (0.27564103 0.72435897)
## 54) symmetry_worst>=-1.327359 20 6 M (0.70000000 0.30000000)
## 108) compactness_se< -2.646661 13 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-2.646661 7 1 B (0.14285714 0.85714286) *
## 55) symmetry_worst< -1.327359 136 29 B (0.21323529 0.78676471)
## 110) texture_worst< 4.530419 84 25 B (0.29761905 0.70238095) *
## 111) texture_worst>=4.530419 52 4 B (0.07692308 0.92307692) *
## 7) texture_worst>=4.747573 62 10 B (0.16129032 0.83870968)
## 14) symmetry_worst>=-1.758895 18 8 B (0.44444444 0.55555556)
## 28) smoothness_mean< -2.321477 10 2 M (0.80000000 0.20000000)
## 56) texture_mean< 3.091538 8 0 M (1.00000000 0.00000000) *
## 57) texture_mean>=3.091538 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.321477 8 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.758895 44 2 B (0.04545455 0.95454545)
## 30) compactness_se>=-2.72933 2 0 M (1.00000000 0.00000000) *
## 31) compactness_se< -2.72933 42 0 B (0.00000000 1.00000000) *
##
##
## $weights
## [1] 1.0143223 0.9756740 0.6544576 0.7289082 0.4903051 0.5550935 0.2876624
## [8] 0.4064599 0.5233357 0.5756598 0.6614213 0.7341538 0.8647840 0.6804425
## [15] 0.2325935 0.5212432 0.5775010 0.5009532 0.7629577 0.5141035 0.4451419
## [22] 0.4937205 0.2743158 0.4192772 0.8325064 0.5171780 0.5715872 0.6065961
## [29] 0.5535083 0.5271030 0.3934626 0.3765380 0.4683799 0.7562199 0.6684508
## [36] 0.5028706 0.5769558 0.5797812 0.4745713 0.4430292 0.5735213 0.3458962
## [43] 0.3509300 0.5811027 0.6716382 0.8078402 0.5685447 0.5013177 0.8080208
## [50] 0.6515440 0.6881326 0.6901804 0.5601635 0.5244748 1.0363324 0.7371558
## [57] 0.3828816 0.6145244 0.6134399 0.4442746 0.8372090 0.6366471 0.7484774
## [64] 0.6330865 0.4469461 0.5757497 0.5598130 0.8043061 0.6113138 0.5318741
## [71] 0.6733939 0.4553865 0.6919234 0.5780642 0.3087808 0.3121142 0.4495589
## [78] 0.5507634 0.3404249 0.5228403 0.4359747 0.5776337 0.5951755 0.5141975
## [85] 0.4873377 0.6716764 0.6972312 0.5777235 0.4797364 0.4103516 0.3885086
## [92] 0.7396938 0.5585543 0.8443694 0.5253073 0.7530113 0.5281069 0.4253419
## [99] 0.6417699 0.5549526
##
## $votes
## [,1] [,2]
## [1,] 42.329044 15.241425
## [2,] 39.490181 18.080288
## [3,] 45.031439 12.539030
## [4,] 42.252155 15.318315
## [5,] 40.758192 16.812278
## [6,] 41.813274 15.757196
## [7,] 41.595879 15.974590
## [8,] 44.876683 12.693787
## [9,] 49.393533 8.176937
## [10,] 39.165566 18.404903
## [11,] 43.046311 14.524159
## [12,] 39.041051 18.529418
## [13,] 47.917722 9.652747
## [14,] 40.380235 17.190235
## [15,] 47.007937 10.562532
## [16,] 15.015397 42.555072
## [17,] 11.843963 45.726507
## [18,] 8.933814 48.636655
## [19,] 40.432408 17.138061
## [20,] 47.539462 10.031007
## [21,] 42.225845 15.344624
## [22,] 46.100072 11.470397
## [23,] 40.375669 17.194801
## [24,] 46.191932 11.378537
## [25,] 40.029218 17.541252
## [26,] 46.495445 11.075024
## [27,] 44.552073 13.018396
## [28,] 40.999030 16.571440
## [29,] 42.426265 15.144204
## [30,] 41.714131 15.856338
## [31,] 16.493060 41.077409
## [32,] 40.690544 16.879925
## [33,] 41.080104 16.490365
## [34,] 39.939305 17.631164
## [35,] 42.745898 14.824571
## [36,] 44.053902 13.516567
## [37,] 15.344474 42.225995
## [38,] 15.177957 42.392512
## [39,] 17.549748 40.020721
## [40,] 15.476259 42.094210
## [41,] 10.545578 47.024891
## [42,] 14.244404 43.326065
## [43,] 44.315197 13.255272
## [44,] 15.426712 42.143757
## [45,] 42.169877 15.400592
## [46,] 46.276418 11.294052
## [47,] 11.109954 46.460516
## [48,] 15.288239 42.282230
## [49,] 17.402916 40.167553
## [50,] 40.908666 16.661803
## [51,] 10.690868 46.879601
## [52,] 46.689719 10.880750
## [53,] 15.710229 41.860240
## [54,] 14.430000 43.140470
## [55,] 44.506806 13.063663
## [56,] 40.078074 17.492395
## [57,] 15.594958 41.975511
## [58,] 41.099115 16.471354
## [59,] 17.651822 39.918647
## [60,] 40.706496 16.863973
## [61,] 43.487699 14.082770
## [62,] 17.649931 39.920538
## [63,] 15.889126 41.681343
## [64,] 44.848782 12.721687
## [65,] 43.443864 14.126605
## [66,] 16.531876 41.038593
## [67,] 40.071184 17.499285
## [68,] 40.415264 17.155205
## [69,] 41.420168 16.150301
## [70,] 16.026344 41.544125
## [71,] 15.487392 42.083078
## [72,] 42.539526 15.030944
## [73,] 17.333677 40.236792
## [74,] 41.764358 15.806111
## [75,] 41.330089 16.240380
## [76,] 15.713683 41.856786
## [77,] 15.523180 42.047289
## [78,] 40.750992 16.819477
## [79,] 41.158934 16.411536
## [80,] 7.723798 49.846671
## [81,] 17.844459 39.726010
## [82,] 41.467870 16.102599
## [83,] 15.628771 41.941699
## [84,] 16.674289 40.896180
## [85,] 17.835032 39.735437
## [86,] 15.581389 41.989081
## [87,] 16.930429 40.640040
## [88,] 15.647728 41.922741
## [89,] 15.413664 42.156805
## [90,] 15.929812 41.640658
## [91,] 43.611474 13.958995
## [92,] 46.226228 11.344241
## [93,] 41.569540 16.000929
## [94,] 16.237257 41.333213
## [95,] 39.627016 17.943453
## [96,] 9.645731 47.924738
## [97,] 15.587883 41.982586
## [98,] 40.970599 16.599870
## [99,] 40.768091 16.802378
## [100,] 16.267338 41.303131
## [101,] 39.061435 18.509034
## [102,] 42.326898 15.243571
## [103,] 16.546250 41.024219
## [104,] 41.636422 15.934047
## [105,] 13.925605 43.644865
## [106,] 14.259043 43.311426
## [107,] 15.745004 41.825465
## [108,] 7.481203 50.089266
## [109,] 39.615987 17.954482
## [110,] 15.931767 41.638703
## [111,] 13.471828 44.098642
## [112,] 8.316276 49.254193
## [113,] 15.865027 41.705443
## [114,] 16.065740 41.504730
## [115,] 15.030440 42.540029
## [116,] 17.436322 40.134148
## [117,] 13.445806 44.124663
## [118,] 10.846829 46.723640
## [119,] 16.188481 41.381989
## [120,] 15.056254 42.514216
## [121,] 40.135844 17.434625
## [122,] 15.778244 41.792225
## [123,] 11.691783 45.878686
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## [125,] 17.513021 40.057449
## [126,] 41.178354 16.392116
## [127,] 17.777314 39.793155
## [128,] 39.085793 18.484676
## [129,] 15.696046 41.874424
## [130,] 39.619420 17.951049
## [131,] 41.692133 15.878336
## [132,] 15.694279 41.876190
## [133,] 11.553010 46.017459
## [134,] 40.291941 17.278529
## [135,] 40.846776 16.723693
## [136,] 16.241672 41.328797
## [137,] 11.874961 45.695508
## [138,] 10.162298 47.408171
## [139,] 12.883071 44.687398
## [140,] 40.158291 17.412178
## [141,] 13.170323 44.400146
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## [143,] 41.918958 15.651511
## [144,] 48.498962 9.071507
## [145,] 41.596852 15.973617
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## [148,] 39.118530 18.451940
## [149,] 15.304620 42.265849
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## [151,] 12.853754 44.716716
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## [173,] 45.553639 12.016831
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## [176,] 6.324357 51.246112
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## [180,] 15.100720 42.469749
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## [188,] 16.939696 40.630774
## [189,] 16.333332 41.237138
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## [192,] 13.099085 44.471384
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## [202,] 46.580196 10.990273
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## [205,] 40.089681 17.480788
## [206,] 15.376680 42.193789
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## [208,] 15.239602 42.330868
## [209,] 17.225327 40.345142
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## [218,] 14.045496 43.524973
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## [722,] 15.171194 42.399275
## [723,] 10.071307 47.499162
## [724,] 40.970157 16.600312
## [725,] 42.510813 15.059656
## [726,] 39.658379 17.912090
## [727,] 17.287970 40.282499
## [728,] 16.317325 41.253144
## [729,] 10.559911 47.010558
## [730,] 12.582730 44.987739
## [731,] 41.943917 15.626552
## [732,] 16.409448 41.161021
## [733,] 41.113161 16.457308
## [734,] 16.095134 41.475335
## [735,] 15.016418 42.554051
## [736,] 16.302920 41.267549
## [737,] 9.252615 48.317854
## [738,] 13.991506 43.578964
## [739,] 16.388889 41.181580
## [740,] 13.425525 44.144944
## [741,] 11.970377 45.600093
## [742,] 44.484780 13.085690
## [743,] 13.092086 44.478384
## [744,] 17.216979 40.353490
## [745,] 6.958250 50.612219
## [746,] 16.103215 41.467254
## [747,] 10.366227 47.204242
## [748,] 15.331872 42.238597
## [749,] 17.619056 39.951413
## [750,] 17.083798 40.486671
## [751,] 40.863937 16.706532
## [752,] 17.718919 39.851550
## [753,] 40.847328 16.723141
## [754,] 40.767173 16.803297
## [755,] 46.609192 10.961278
## [756,] 40.092075 17.478394
## [757,] 40.468933 17.101537
## [758,] 15.927028 41.643441
## [759,] 13.194750 44.375719
## [760,] 15.644265 41.926204
## [761,] 14.827984 42.742485
## [762,] 13.780428 43.790041
## [763,] 48.692716 8.877753
## [764,] 12.853359 44.717110
## [765,] 17.070884 40.499585
## [766,] 14.479401 43.091068
## [767,] 40.006217 17.564252
## [768,] 10.294627 47.275842
## [769,] 13.735708 43.834761
## [770,] 41.678969 15.891500
## [771,] 10.508065 47.062404
## [772,] 16.516279 41.054191
## [773,] 42.340078 15.230392
## [774,] 46.045008 11.525462
## [775,] 17.768793 39.801676
## [776,] 12.280889 45.289580
## [777,] 14.388007 43.182462
## [778,] 11.805312 45.765157
## [779,] 18.261665 39.308804
## [780,] 41.854440 15.716029
## [781,] 14.563284 43.007185
## [782,] 13.342936 44.227534
## [783,] 16.874479 40.695990
## [784,] 15.461922 42.108547
## [785,] 43.126498 14.443971
## [786,] 15.720124 41.850345
## [787,] 16.125408 41.445061
## [788,] 13.679993 43.890476
## [789,] 16.623213 40.947256
## [790,] 39.828878 17.741592
## [791,] 17.358376 40.212093
## [792,] 13.405963 44.164506
## [793,] 15.687104 41.883365
## [794,] 16.637079 40.933390
## [795,] 15.389878 42.180591
## [796,] 16.502112 41.068357
## [797,] 16.305862 41.264607
## [798,] 13.452438 44.118032
## [799,] 15.366334 42.204135
## [800,] 17.151604 40.418865
## [801,] 6.399325 51.171144
## [802,] 11.830629 45.739841
## [803,] 40.884001 16.686468
## [804,] 16.932959 40.637510
## [805,] 45.189026 12.381443
## [806,] 45.402719 12.167750
## [807,] 16.024377 41.546092
## [808,] 41.435068 16.135401
## [809,] 16.948472 40.621998
## [810,] 15.730409 41.840060
## [811,] 14.845214 42.725255
## [812,] 11.166136 46.404333
## [813,] 41.544879 16.025590
## [814,] 12.529233 45.041236
## [815,] 17.258650 40.311819
## [816,] 38.772826 18.797643
## [817,] 39.802093 17.768376
## [818,] 13.463067 44.107402
## [819,] 11.860380 45.710089
## [820,] 39.423343 18.147127
## [821,] 17.141117 40.429352
## [822,] 40.789358 16.781111
## [823,] 17.613195 39.957275
## [824,] 12.639034 44.931435
## [825,] 18.047056 39.523413
## [826,] 15.492260 42.078210
## [827,] 15.925533 41.644936
## [828,] 12.695213 44.875256
## [829,] 42.214018 15.356451
## [830,] 11.499153 46.071316
## [831,] 16.096041 41.474429
## [832,] 15.943344 41.627125
## [833,] 18.420080 39.150390
## [834,] 16.695493 40.874976
## [835,] 41.226719 16.343751
## [836,] 16.714149 40.856320
## [837,] 15.985564 41.584905
## [838,] 14.683016 42.887453
## [839,] 12.589432 44.981037
## [840,] 16.952283 40.618186
## [841,] 14.133844 43.436626
## [842,] 40.301270 17.269199
## [843,] 15.368830 42.201639
## [844,] 15.785327 41.785142
## [845,] 16.975047 40.595422
## [846,] 15.752889 41.817580
## [847,] 10.548220 47.022249
## [848,] 14.368208 43.202261
## [849,] 46.342119 11.228350
## [850,] 11.527286 46.043183
## [851,] 42.000720 15.569750
## [852,] 40.382347 17.188122
## [853,] 7.428649 50.141820
## [854,] 13.691033 43.879436
## [855,] 15.718955 41.851514
## [856,] 16.378958 41.191511
## [857,] 41.758494 15.811975
## [858,] 16.117102 41.453367
## [859,] 39.822562 17.747907
## [860,] 16.322066 41.248403
## [861,] 15.027980 42.542490
## [862,] 18.514879 39.055590
## [863,] 16.306859 41.263610
## [864,] 43.721618 13.848851
## [865,] 8.997124 48.573345
## [866,] 12.453205 45.117264
## [867,] 45.263275 12.307195
## [868,] 14.433005 43.137464
## [869,] 40.072182 17.498288
## [870,] 15.927920 41.642549
## [871,] 41.736655 15.833815
## [872,] 41.480181 16.090288
## [873,] 15.844739 41.725730
## [874,] 16.785236 40.785233
## [875,] 40.065990 17.504479
## [876,] 14.849806 42.720663
## [877,] 17.065346 40.505123
## [878,] 10.247147 47.323322
## [879,] 17.685187 39.885282
## [880,] 8.112941 49.457529
## [881,] 16.438828 41.131641
## [882,] 18.344187 39.226282
## [883,] 14.295648 43.274821
## [884,] 40.367789 17.202680
## [885,] 15.223388 42.347081
## [886,] 16.907704 40.662766
## [887,] 15.586970 41.983499
## [888,] 14.215714 43.354755
## [889,] 18.114428 39.456042
## [890,] 16.348660 41.221809
## [891,] 15.930001 41.640468
## [892,] 18.425627 39.144842
## [893,] 13.531725 44.038744
## [894,] 17.526021 40.044449
## [895,] 15.619177 41.951293
## [896,] 15.159378 42.411091
## [897,] 16.940786 40.629683
## [898,] 16.673553 40.896916
## [899,] 16.390306 41.180164
## [900,] 15.724532 41.845937
## [901,] 14.799221 42.771248
## [902,] 18.300022 39.270447
## [903,] 14.353778 43.216691
## [904,] 13.852387 43.718082
## [905,] 16.834420 40.736049
## [906,] 17.289087 40.281382
## [907,] 13.422934 44.147536
## [908,] 49.214369 8.356100
## [909,] 42.330540 15.239929
## [910,] 41.158148 16.412322
## [911,] 40.125715 17.444754
## [912,] 47.532105 10.038364
##
## $prob
## [,1] [,2]
## [1,] 0.7352562 0.2647438
## [2,] 0.6859451 0.3140549
## [3,] 0.7821968 0.2178032
## [4,] 0.7339206 0.2660794
## [5,] 0.7079705 0.2920295
## [6,] 0.7262973 0.2737027
## [7,] 0.7225211 0.2774789
## [8,] 0.7795087 0.2204913
## [9,] 0.8579665 0.1420335
## [10,] 0.6803065 0.3196935
## [11,] 0.7477151 0.2522849
## [12,] 0.6781437 0.3218563
## [13,] 0.8323316 0.1676684
## [14,] 0.7014053 0.2985947
## [15,] 0.8165286 0.1834714
## [16,] 0.2608177 0.7391823
## [17,] 0.2057298 0.7942702
## [18,] 0.1551805 0.8448195
## [19,] 0.7023116 0.2976884
## [20,] 0.8257612 0.1742388
## [21,] 0.7334636 0.2665364
## [22,] 0.8007590 0.1992410
## [23,] 0.7013260 0.2986740
## [24,] 0.8023546 0.1976454
## [25,] 0.6953082 0.3046918
## [26,] 0.8076267 0.1923733
## [27,] 0.7738702 0.2261298
## [28,] 0.7121538 0.2878462
## [29,] 0.7369449 0.2630551
## [30,] 0.7245751 0.2754249
## [31,] 0.2864847 0.7135153
## [32,] 0.7067954 0.2932046
## [33,] 0.7135621 0.2864379
## [34,] 0.6937464 0.3062536
## [35,] 0.7424970 0.2575030
## [36,] 0.7652170 0.2347830
## [37,] 0.2665338 0.7334662
## [38,] 0.2636414 0.7363586
## [39,] 0.3048394 0.6951606
## [40,] 0.2688229 0.7311771
## [41,] 0.1831769 0.8168231
## [42,] 0.2474255 0.7525745
## [43,] 0.7697557 0.2302443
## [44,] 0.2679622 0.7320378
## [45,] 0.7324915 0.2675085
## [46,] 0.8038221 0.1961779
## [47,] 0.1929801 0.8070199
## [48,] 0.2655570 0.7344430
## [49,] 0.3022889 0.6977111
## [50,] 0.7105842 0.2894158
## [51,] 0.1857006 0.8142994
## [52,] 0.8110012 0.1889988
## [53,] 0.2728869 0.7271131
## [54,] 0.2506493 0.7493507
## [55,] 0.7730840 0.2269160
## [56,] 0.6961568 0.3038432
## [57,] 0.2708847 0.7291153
## [58,] 0.7138923 0.2861077
## [59,] 0.3066124 0.6933876
## [60,] 0.7070725 0.2929275
## [61,] 0.7553821 0.2446179
## [62,] 0.3065796 0.6934204
## [63,] 0.2759944 0.7240056
## [64,] 0.7790241 0.2209759
## [65,] 0.7546206 0.2453794
## [66,] 0.2871590 0.7128410
## [67,] 0.6960371 0.3039629
## [68,] 0.7020138 0.2979862
## [69,] 0.7194690 0.2805310
## [70,] 0.2783779 0.7216221
## [71,] 0.2690162 0.7309838
## [72,] 0.7389123 0.2610877
## [73,] 0.3010863 0.6989137
## [74,] 0.7254476 0.2745524
## [75,] 0.7179043 0.2820957
## [76,] 0.2729469 0.7270531
## [77,] 0.2696379 0.7303621
## [78,] 0.7078454 0.2921546
## [79,] 0.7149314 0.2850686
## [80,] 0.1341625 0.8658375
## [81,] 0.3099585 0.6900415
## [82,] 0.7202976 0.2797024
## [83,] 0.2714720 0.7285280
## [84,] 0.2896327 0.7103673
## [85,] 0.3097948 0.6902052
## [86,] 0.2706490 0.7293510
## [87,] 0.2940818 0.7059182
## [88,] 0.2718013 0.7281987
## [89,] 0.2677356 0.7322644
## [90,] 0.2767011 0.7232989
## [91,] 0.7575320 0.2424680
## [92,] 0.8029504 0.1970496
## [93,] 0.7220636 0.2779364
## [94,] 0.2820414 0.7179586
## [95,] 0.6883219 0.3116781
## [96,] 0.1675465 0.8324535
## [97,] 0.2707618 0.7292382
## [98,] 0.7116600 0.2883400
## [99,] 0.7081424 0.2918576
## [100,] 0.2825639 0.7174361
## [101,] 0.6784978 0.3215022
## [102,] 0.7352189 0.2647811
## [103,] 0.2874086 0.7125914
## [104,] 0.7232253 0.2767747
## [105,] 0.2418880 0.7581120
## [106,] 0.2476798 0.7523202
## [107,] 0.2734910 0.7265090
## [108,] 0.1299486 0.8700514
## [109,] 0.6881303 0.3118697
## [110,] 0.2767350 0.7232650
## [111,] 0.2340059 0.7659941
## [112,] 0.1444539 0.8555461
## [113,] 0.2755758 0.7244242
## [114,] 0.2790622 0.7209378
## [115,] 0.2610790 0.7389210
## [116,] 0.3028692 0.6971308
## [117,] 0.2335539 0.7664461
## [118,] 0.1884096 0.8115904
## [119,] 0.2811942 0.7188058
## [120,] 0.2615274 0.7384726
## [121,] 0.6971603 0.3028397
## [122,] 0.2740684 0.7259316
## [123,] 0.2030865 0.7969135
## [124,] 0.1963638 0.8036362
## [125,] 0.3042015 0.6957985
## [126,] 0.7152687 0.2847313
## [127,] 0.3087922 0.6912078
## [128,] 0.6789209 0.3210791
## [129,] 0.2726406 0.7273594
## [130,] 0.6881900 0.3118100
## [131,] 0.7241930 0.2758070
## [132,] 0.2726099 0.7273901
## [133,] 0.2006760 0.7993240
## [134,] 0.6998717 0.3001283
## [135,] 0.7095092 0.2904908
## [136,] 0.2821181 0.7178819
## [137,] 0.2062683 0.7937317
## [138,] 0.1765193 0.8234807
## [139,] 0.2237792 0.7762208
## [140,] 0.6975502 0.3024498
## [141,] 0.2287687 0.7712313
## [142,] 0.1632495 0.8367505
## [143,] 0.7281330 0.2718670
## [144,] 0.8424278 0.1575722
## [145,] 0.7225380 0.2774620
## [146,] 0.2367717 0.7632283
## [147,] 0.2881953 0.7118047
## [148,] 0.6794895 0.3205105
## [149,] 0.2658415 0.7341585
## [150,] 0.2869089 0.7130911
## [151,] 0.2232699 0.7767301
## [152,] 0.2525085 0.7474915
## [153,] 0.2357005 0.7642995
## [154,] 0.7380869 0.2619131
## [155,] 0.3174177 0.6825823
## [156,] 0.7080855 0.2919145
## [157,] 0.6856225 0.3143775
## [158,] 0.7001350 0.2998650
## [159,] 0.8151586 0.1848414
## [160,] 0.7860496 0.2139504
## [161,] 0.3069449 0.6930551
## [162,] 0.6831266 0.3168734
## [163,] 0.2413487 0.7586513
## [164,] 0.7298137 0.2701863
## [165,] 0.1891569 0.8108431
## [166,] 0.2992935 0.7007065
## [167,] 0.7281378 0.2718622
## [168,] 0.7714745 0.2285255
## [169,] 0.7404177 0.2595823
## [170,] 0.2311786 0.7688214
## [171,] 0.7105385 0.2894615
## [172,] 0.7084727 0.2915273
## [173,] 0.7912675 0.2087325
## [174,] 0.2772646 0.7227354
## [175,] 0.1619016 0.8380984
## [176,] 0.1098542 0.8901458
## [177,] 0.2880209 0.7119791
## [178,] 0.7545375 0.2454625
## [179,] 0.7201753 0.2798247
## [180,] 0.2622998 0.7377002
## [181,] 0.2635231 0.7364769
## [182,] 0.6868494 0.3131506
## [183,] 0.2891135 0.7108865
## [184,] 0.2877927 0.7122073
## [185,] 0.7197773 0.2802227
## [186,] 0.7047148 0.2952852
## [187,] 0.2869416 0.7130584
## [188,] 0.2942428 0.7057572
## [189,] 0.2837102 0.7162898
## [190,] 0.7367966 0.2632034
## [191,] 0.2954235 0.7045765
## [192,] 0.2275313 0.7724687
## [193,] 0.3224772 0.6775228
## [194,] 0.2704900 0.7295100
## [195,] 0.7135887 0.2864113
## [196,] 0.3037840 0.6962160
## [197,] 0.7260138 0.2739862
## [198,] 0.6793229 0.3206771
## [199,] 0.7024898 0.2975102
## [200,] 0.7209358 0.2790642
## [201,] 0.7194634 0.2805366
## [202,] 0.8090988 0.1909012
## [203,] 0.6894222 0.3105778
## [204,] 0.6885965 0.3114035
## [205,] 0.6963584 0.3036416
## [206,] 0.2670932 0.7329068
## [207,] 0.2225523 0.7774477
## [208,] 0.2647121 0.7352879
## [209,] 0.2992042 0.7007958
## [210,] 0.1760127 0.8239873
## [211,] 0.7278292 0.2721708
## [212,] 0.2076513 0.7923487
## [213,] 0.2542866 0.7457134
## [214,] 0.6883860 0.3116140
## [215,] 0.1908199 0.8091801
## [216,] 0.2105511 0.7894489
## [217,] 0.7895635 0.2104365
## [218,] 0.2439705 0.7560295
## [219,] 0.7216484 0.2783516
## [220,] 0.6903812 0.3096188
## [221,] 0.1901399 0.8098601
## [222,] 0.2696729 0.7303271
## [223,] 0.1244225 0.8755775
## [224,] 0.2767746 0.7232254
## [225,] 0.2845646 0.7154354
## [226,] 0.2367173 0.7632827
## [227,] 0.2979274 0.7020726
## [228,] 0.2898072 0.7101928
## [229,] 0.2517135 0.7482865
## [230,] 0.1848676 0.8151324
## [231,] 0.1458994 0.8541006
## [232,] 0.1503106 0.8496894
## [233,] 0.6945207 0.3054793
## [234,] 0.2403908 0.7596092
## [235,] 0.7021025 0.2978975
## [236,] 0.2647462 0.7352538
## [237,] 0.7185263 0.2814737
## [238,] 0.2618098 0.7381902
## [239,] 0.2724274 0.7275726
## [240,] 0.2682572 0.7317428
## [241,] 0.1803455 0.8196545
## [242,] 0.1350553 0.8649447
## [243,] 0.1243218 0.8756782
## [244,] 0.2847611 0.7152389
## [245,] 0.1184873 0.8815127
## [246,] 0.2865372 0.7134628
## [247,] 0.2514061 0.7485939
## [248,] 0.1756502 0.8243498
## [249,] 0.2074044 0.7925956
## [250,] 0.1092836 0.8907164
## [251,] 0.7112454 0.2887546
## [252,] 0.2782223 0.7217777
## [253,] 0.2153788 0.7846212
## [254,] 0.2528789 0.7471211
## [255,] 0.6953370 0.3046630
## [256,] 0.2329943 0.7670057
## [257,] 0.8447556 0.1552444
## [258,] 0.2635239 0.7364761
## [259,] 0.2362117 0.7637883
## [260,] 0.1585276 0.8414724
## [261,] 0.7116523 0.2883477
## [262,] 0.7384135 0.2615865
## [263,] 0.6888667 0.3111333
## [264,] 0.3002923 0.6997077
## [265,] 0.2834322 0.7165678
## [266,] 0.1834258 0.8165742
## [267,] 0.2185622 0.7814378
## [268,] 0.6996754 0.3003246
## [269,] 0.2917710 0.7082290
## [270,] 0.7285665 0.2714335
## [271,] 0.2850324 0.7149676
## [272,] 0.7141363 0.2858637
## [273,] 0.2795727 0.7204273
## [274,] 0.2608354 0.7391646
## [275,] 0.2831820 0.7168180
## [276,] 0.7268317 0.2731683
## [277,] 0.2430327 0.7569673
## [278,] 0.2846753 0.7153247
## [279,] 0.2015715 0.7984285
## [280,] 0.2866106 0.7133894
## [281,] 0.2079256 0.7920744
## [282,] 0.7727014 0.2272986
## [283,] 0.7467163 0.2532837
## [284,] 0.7122979 0.2877021
## [285,] 0.2274097 0.7725903
## [286,] 0.2990592 0.7009408
## [287,] 0.1208649 0.8791351
## [288,] 0.2841664 0.7158336
## [289,] 0.2797131 0.7202869
## [290,] 0.1800615 0.8199385
## [291,] 0.2663149 0.7336851
## [292,] 0.3060433 0.6939567
## [293,] 0.7098073 0.2901927
## [294,] 0.7620860 0.2379140
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## [768,] 0.1788178 0.8211822
## [769,] 0.2385895 0.7614105
## [770,] 0.7239644 0.2760356
## [771,] 0.1825253 0.8174747
## [772,] 0.2868880 0.7131120
## [773,] 0.7354478 0.2645522
## [774,] 0.7998025 0.2001975
## [775,] 0.3086442 0.6913558
## [776,] 0.2133192 0.7866808
## [777,] 0.2499199 0.7500801
## [778,] 0.2050585 0.7949415
## [779,] 0.3172054 0.6827946
## [780,] 0.7270123 0.2729877
## [781,] 0.2529645 0.7470355
## [782,] 0.2317670 0.7682330
## [783,] 0.2931100 0.7068900
## [784,] 0.2685738 0.7314262
## [785,] 0.7491080 0.2508920
## [786,] 0.2730588 0.7269412
## [787,] 0.2800986 0.7199014
## [788,] 0.2376217 0.7623783
## [789,] 0.2887455 0.7112545
## [790,] 0.6918283 0.3081717
## [791,] 0.3015153 0.6984847
## [792,] 0.2328618 0.7671382
## [793,] 0.2724853 0.7275147
## [794,] 0.2889863 0.7110137
## [795,] 0.2673224 0.7326776
## [796,] 0.2866420 0.7133580
## [797,] 0.2832331 0.7167669
## [798,] 0.2336691 0.7663309
## [799,] 0.2669135 0.7330865
## [800,] 0.2979237 0.7020763
## [801,] 0.1111564 0.8888436
## [802,] 0.2054982 0.7945018
## [803,] 0.7101558 0.2898442
## [804,] 0.2941258 0.7058742
## [805,] 0.7849341 0.2150659
## [806,] 0.7886460 0.2113540
## [807,] 0.2783437 0.7216563
## [808,] 0.7197278 0.2802722
## [809,] 0.2943952 0.7056048
## [810,] 0.2732375 0.7267625
## [811,] 0.2578616 0.7421384
## [812,] 0.1939560 0.8060440
## [813,] 0.7216352 0.2783648
## [814,] 0.2176330 0.7823670
## [815,] 0.2997830 0.7002170
## [816,] 0.6734846 0.3265154
## [817,] 0.6913630 0.3086370
## [818,] 0.2338537 0.7661463
## [819,] 0.2060150 0.7939850
## [820,] 0.6847841 0.3152159
## [821,] 0.2977415 0.7022585
## [822,] 0.7085118 0.2914882
## [823,] 0.3059415 0.6940585
## [824,] 0.2195402 0.7804598
## [825,] 0.3134777 0.6865223
## [826,] 0.2691008 0.7308992
## [827,] 0.2766268 0.7233732
## [828,] 0.2205161 0.7794839
## [829,] 0.7332582 0.2667418
## [830,] 0.1997405 0.8002595
## [831,] 0.2795885 0.7204115
## [832,] 0.2769361 0.7230639
## [833,] 0.3199571 0.6800429
## [834,] 0.2900010 0.7099990
## [835,] 0.7161088 0.2838912
## [836,] 0.2903250 0.7096750
## [837,] 0.2776695 0.7223305
## [838,] 0.2550442 0.7449558
## [839,] 0.2186786 0.7813214
## [840,] 0.2944614 0.7055386
## [841,] 0.2455051 0.7544949
## [842,] 0.7000337 0.2999663
## [843,] 0.2669568 0.7330432
## [844,] 0.2741914 0.7258086
## [845,] 0.2948568 0.7051432
## [846,] 0.2736279 0.7263721
## [847,] 0.1832228 0.8167772
## [848,] 0.2495760 0.7504240
## [849,] 0.8049634 0.1950366
## [850,] 0.2002292 0.7997708
## [851,] 0.7295532 0.2704468
## [852,] 0.7014420 0.2985580
## [853,] 0.1290358 0.8709642
## [854,] 0.2378135 0.7621865
## [855,] 0.2730385 0.7269615
## [856,] 0.2845028 0.7154972
## [857,] 0.7253457 0.2746543
## [858,] 0.2799543 0.7200457
## [859,] 0.6917186 0.3082814
## [860,] 0.2835146 0.7164854
## [861,] 0.2610363 0.7389637
## [862,] 0.3216038 0.6783962
## [863,] 0.2832504 0.7167496
## [864,] 0.7594452 0.2405548
## [865,] 0.1562802 0.8437198
## [866,] 0.2163124 0.7836876
## [867,] 0.7862238 0.2137762
## [868,] 0.2507015 0.7492985
## [869,] 0.6960545 0.3039455
## [870,] 0.2766682 0.7233318
## [871,] 0.7249664 0.2750336
## [872,] 0.7205114 0.2794886
## [873,] 0.2752234 0.7247766
## [874,] 0.2915598 0.7084402
## [875,] 0.6959469 0.3040531
## [876,] 0.2579414 0.7420586
## [877,] 0.2964253 0.7035747
## [878,] 0.1779931 0.8220069
## [879,] 0.3071920 0.6928080
## [880,] 0.1409219 0.8590781
## [881,] 0.2855427 0.7144573
## [882,] 0.3186388 0.6813612
## [883,] 0.2483156 0.7516844
## [884,] 0.7011892 0.2988108
## [885,] 0.2644305 0.7355695
## [886,] 0.2936871 0.7063129
## [887,] 0.2707459 0.7292541
## [888,] 0.2469272 0.7530728
## [889,] 0.3146479 0.6853521
## [890,] 0.2839765 0.7160235
## [891,] 0.2767044 0.7232956
## [892,] 0.3200535 0.6799465
## [893,] 0.2350463 0.7649537
## [894,] 0.3044273 0.6955727
## [895,] 0.2713054 0.7286946
## [896,] 0.2633186 0.7366814
## [897,] 0.2942617 0.7057383
## [898,] 0.2896199 0.7103801
## [899,] 0.2846999 0.7153001
## [900,] 0.2731354 0.7268646
## [901,] 0.2570627 0.7429373
## [902,] 0.3178717 0.6821283
## [903,] 0.2493254 0.7506746
## [904,] 0.2406162 0.7593838
## [905,] 0.2924142 0.7075858
## [906,] 0.3003117 0.6996883
## [907,] 0.2331566 0.7668434
## [908,] 0.8548544 0.1451456
## [909,] 0.7352822 0.2647178
## [910,] 0.7149177 0.2850823
## [911,] 0.6969843 0.3030157
## [912,] 0.8256335 0.1743665
##
## $class
## [1] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "B" "B"
## [19] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "M" "M" "M" "M" "M"
## [37] "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "B" "B" "M" "B" "M" "B" "B"
## [55] "M" "M" "B" "M" "B" "M" "M" "B" "B" "M" "M" "B" "M" "M" "M" "B" "B" "M"
## [73] "B" "M" "M" "B" "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B"
## [91] "M" "M" "M" "B" "M" "B" "B" "M" "M" "B" "M" "M" "B" "M" "B" "B" "B" "B"
## [109] "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M"
## [127] "B" "M" "B" "M" "M" "B" "B" "M" "M" "B" "B" "B" "B" "M" "B" "B" "M" "M"
## [145] "M" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B" "M" "M" "M" "M" "M" "B" "M"
## [163] "B" "M" "B" "B" "M" "M" "M" "B" "M" "M" "M" "B" "B" "B" "B" "M" "M" "B"
## [181] "B" "M" "B" "B" "M" "M" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "M" "M"
## [199] "M" "M" "M" "M" "M" "M" "M" "B" "B" "B" "B" "B" "M" "B" "B" "M" "B" "B"
## [217] "M" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B"
## [235] "M" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B"
## [253] "B" "B" "M" "B" "M" "B" "B" "B" "M" "M" "M" "B" "B" "B" "B" "M" "B" "M"
## [271] "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "M" "M" "B" "B" "B" "B"
## [289] "B" "B" "B" "B" "M" "M" "M" "M" "M" "B" "M" "M" "B" "B" "B" "B" "B" "M"
## [307] "B" "B" "B" "M" "B" "M" "B" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B"
## [325] "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B"
## [343] "B" "B" "B" "B" "B" "M" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M"
## [361] "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B"
## [379] "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "M" "M" "B"
## [397] "B" "B" "B" "M" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "M"
## [415] "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B"
## [433] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M"
## [451] "M" "B" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B"
## [469] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "M" "M" "M" "M"
## [487] "M" "M" "M" "B" "B" "B" "B" "B" "M" "M" "M" "M" "B" "B" "B" "B" "M" "M"
## [505] "B" "B" "B" "B" "M" "B" "M" "M" "B" "M" "M" "M" "B" "B" "M" "M" "B" "M"
## [523] "M" "B" "B" "B" "M" "B" "M" "B" "B" "B" "M" "M" "B" "B" "B" "B" "M" "B"
## [541] "M" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "B" "M" "M" "B" "M" "B"
## [559] "M" "B" "M" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B"
## [577] "B" "B" "B" "B" "B" "B" "M" "M" "B" "B" "B" "M" "M" "B" "B" "M" "M" "B"
## [595] "B" "B" "B" "B" "B" "M" "M" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M"
## [613] "M" "M" "M" "M" "B" "M" "M" "M" "B" "B" "M" "B" "B" "B" "M" "M" "M" "M"
## [631] "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "M" "M" "B" "B" "M" "B" "B" "M"
## [649] "M" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "M" "M" "M" "M" "M" "M" "M"
## [667] "M" "M" "M" "M" "M" "B" "B" "B" "B" "B" "B" "M" "B" "M" "B" "B" "M" "B"
## [685] "B" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [703] "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B"
## [721] "M" "B" "B" "M" "M" "M" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B"
## [739] "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "M" "M"
## [757] "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B" "B" "M" "B" "B" "M" "M"
## [775] "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B"
## [793] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "M" "B" "B"
## [811] "B" "B" "M" "B" "B" "M" "M" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B"
## [829] "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B"
## [847] "B" "B" "M" "B" "M" "M" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "M"
## [865] "B" "B" "M" "B" "M" "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B"
## [883] "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [901] "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M"
##
## $importance
## compactness_se smoothness_mean smoothness_worst symmetry_worst
## 18.47356 17.49196 17.67883 16.51511
## texture_mean texture_worst
## 16.75694 13.08360
##
## $terms
## .outcome ~ texture_mean + smoothness_mean + compactness_se +
## texture_worst + smoothness_worst + symmetry_worst
## attr(,"variables")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"factors")
## texture_mean smoothness_mean compactness_se texture_worst
## .outcome 0 0 0 0
## texture_mean 1 0 0 0
## smoothness_mean 0 1 0 0
## compactness_se 0 0 1 0
## texture_worst 0 0 0 1
## smoothness_worst 0 0 0 0
## symmetry_worst 0 0 0 0
## smoothness_worst symmetry_worst
## .outcome 0 0
## texture_mean 0 0
## smoothness_mean 0 0
## compactness_se 0 0
## texture_worst 0 0
## smoothness_worst 1 0
## symmetry_worst 0 1
## attr(,"term.labels")
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
## attr(,"order")
## [1] 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: 0x000000003ec71dd8>
## attr(,"predvars")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"dataClasses")
## .outcome texture_mean smoothness_mean compactness_se
## "factor" "numeric" "numeric" "numeric"
## texture_worst smoothness_worst symmetry_worst
## "numeric" "numeric" "numeric"
##
## $call
## (function (formula, data, boos = TRUE, mfinal = 100, coeflearn = "Breiman",
## control, ...)
## {
## if (!(as.character(coeflearn) %in% c("Freund", "Breiman",
## "Zhu"))) {
## stop("coeflearn must be 'Freund', 'Breiman' or 'Zhu' ")
## }
## formula <- as.formula(formula)
## vardep <- data[, as.character(formula[[2]])]
## n <- length(data[, 1])
## nclases <- nlevels(vardep)
## pesos <- rep(1/n, n)
## guardarpesos <- array(0, c(n, mfinal))
## w <- rep(1/n, n)
## data <- cbind(pesos, data)
## arboles <- list()
## pond <- rep(0, mfinal)
## pred <- data.frame(rep(0, n))
## arboles[[1]] <- rpart(formula, data = data[, -1], control = rpart.control(minsplit = 1,
## cp = -1, maxdepth = 30))
## nvar <- dim(varImp(arboles[[1]], surrogates = FALSE, competes = FALSE))[1]
## imp <- array(0, c(mfinal, nvar))
## for (m in 1:mfinal) {
## if (boos == TRUE) {
## k <- 1
## while (k == 1) {
## boostrap <- sample(1:n, replace = TRUE, prob = pesos)
## fit <- rpart(formula, data = data[boostrap, -1],
## control = control)
## k <- length(fit$frame$var)
## }
## flearn <- predict(fit, newdata = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## if (boos == FALSE) {
## w <<- pesos
## fit <- rpart(formula = formula, data = data[, -1],
## weights = w, control = control)
## flearn <- predict(fit, data = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## c <- log((1 - err)/err)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## guardarpesos[, m] <- pesos
## pesos <- pesos * exp(c * ind)
## pesos <- pesos/sum(pesos)
## maxerror <- 0.5
## eac <- 0.001
## if (coeflearn == "Zhu") {
## maxerror <- 1 - 1/nclases
## }
## if (err >= maxerror) {
## pesos <- rep(1/n, n)
## maxerror <- maxerror - eac
## c <- log((1 - maxerror)/maxerror)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## if (err == 0) {
## pesos <- rep(1/n, n)
## c <- log((1 - eac)/eac)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## arboles[[m]] <- fit
## pond[m] <- c
## if (m == 1) {
## pred <- flearn
## }
## else {
## pred <- data.frame(pred, flearn)
## }
## if (length(fit$frame$var) > 1) {
## k <- varImp(fit, surrogates = FALSE, competes = FALSE)
## imp[m, ] <- k[sort(row.names(k)), ]
## }
## else {
## imp[m, ] <- rep(0, nvar)
## }
## }
## classfinal <- array(0, c(n, nlevels(vardep)))
## for (i in 1:nlevels(vardep)) {
## classfinal[, i] <- matrix(as.numeric(pred == levels(vardep)[i]),
## nrow = n) %*% as.vector(pond)
## }
## predclass <- rep("O", n)
## predclass[] <- apply(classfinal, 1, FUN = select, vardep.summary = summary(vardep))
## imppond <- as.vector(as.vector(pond) %*% imp)
## imppond <- imppond/sum(imppond) * 100
## names(imppond) <- sort(row.names(k))
## votosporc <- classfinal/apply(classfinal, 1, sum)
## ans <- list(formula = formula, trees = arboles, weights = pond,
## votes = classfinal, prob = votosporc, class = predclass,
## importance = imppond)
## attr(ans, "vardep.summary") <- summary(vardep, maxsum = 700)
## mf <- model.frame(formula = formula, data = data[, -1])
## terms <- attr(mf, "terms")
## ans$terms <- terms
## ans$call <- match.call()
## class(ans) <- "boosting"
## ans
## })(formula = .outcome ~ ., data = list(texture_mean = c(2.33988087773774,
## 2.87751164216656, 3.05635689537043, 3.01455402779458, 2.66305283517147,
## 2.75366071235426, 2.99473177322041, 3.08282698040492, 3.17971910966701,
## 3.14587493198371, 2.88424189752063, 3.17596832385692, 3.11839228628988,
## 3.0022112396517, 3.02916704964023, 2.66444656362008, 2.75429745226753,
## 2.52091708731103, 2.65745841498615, 3.0624559055969, 2.79728133483015,
## 3.06944731137627, 3.00815479355255, 3.2296179214001, 2.71137799119488,
## 2.92852352386054, 3.17722014959937, 3.27601201623901, 2.88368276974537,
## 3.07223024452672, 2.91343703082716, 3.22684399451738, 3.03591406318682,
## 3.07176695982999, 3.21124679770371, 3.08236858021354, 2.82375700881418,
## 2.68307421503203, 3.10458667846607, 3.07269331469012, 2.79361608943186,
## 2.90361698464619, 3.09195113129453, 2.93119375241642, 2.92154737536461,
## 3.07223024452672, 2.96062309644042, 2.46725171454928, 3.04356960296815,
## 3.09783749649114, 2.62900699376176, 3.17555070012983, 3.04499851485691,
## 2.94654202936322, 3.19948911106801, 2.75937682826755, 2.80457176809283,
## 2.97807733831527, 2.39242579699384, 2.78192004966867, 3.17680304844629,
## 2.89037175789616, 3.04309284491383, 3.21526932927409, 3.26918863874179,
## 2.75047091698616, 2.91885122921803, 3.06619073720255, 3.2023398562281,
## 2.7239235502585, 3.17888681665184, 3.12500460925813, 2.90690105984738,
## 2.9871959425317, 3.13679771383259, 2.88144312715186, 2.55256529826182,
## 2.98416563718253, 3.21807550469743, 2.59749101053515, 2.95958682691764,
## 2.74470351875025, 2.91993056013771, 3.0568273729138, 2.83262493568384,
## 3.03302805829769, 2.97807733831527, 3.00518743232475, 2.76190687389292,
## 2.75747508442973, 2.8136106967627, 3.1315734964654, 2.99623214859564,
## 2.38139627341834, 2.8402473707136, 2.38784493694487, 2.84549061022345,
## 3.20639830335709, 2.93969088267037, 2.58701187272515, 2.96938829821439,
## 3.06991167172824, 2.63404478779171, 3.08694315360738, 2.8136106967627,
## 2.73371794785079, 2.59450815970308, 2.48240351956988, 2.89314568477889,
## 2.85128436918812, 2.76757618041624, 2.70604819843154, 2.68444033546308,
## 2.93225985059842, 3.03399098567108, 3.03013370027132, 2.73046379593911,
## 2.57108434602905, 2.73046379593911, 2.88703285663065, 3.03206420280138,
## 2.96836107675786, 2.54474665014402, 2.56186769092413, 3.00469201492546,
## 2.89867056071086, 3.10099278421148, 3.09285898428471, 2.98365969231972,
## 2.9338568698359, 3.20599319903719, 2.83026783382646, 2.51688969564105,
## 2.97705900828837, 2.47569771070269, 2.68852753461335, 2.71800053195538,
## 2.67069441455844, 2.89369954798884, 3.00121720378456, 3.10099278421148,
## 2.56955412384829, 3.08511583468868, 3.27978275977172, 3.01111337559229,
## 2.70270259477561, 2.71535677628465, 2.92208573338569, 2.84432781939476,
## 2.85589532836619, 2.76631910922619, 3.06385810260159, 2.90251989183181,
## 3.14458322028635, 2.79300390698237, 3.0837431508767, 3.11307076597122,
## 2.96114082878437, 3.28353933819392, 3.16758253048065, 2.92316158071916,
## 2.8142103969306, 2.84897089215859, 3.00864849882054, 2.55800220485855,
## 2.94127608775793, 3.24102862950933, 3.17010566049877, 2.82908719614504,
## 2.86105737022739, 3.0708397460408, 3.48031658611475, 3.00815479355255,
## 2.83438912314523, 2.60046499042227, 2.73825604315928, 3.17680304844629,
## 3.10593106585207, 2.94864066602014, 3.29879544804407, 3.52075661671979,
## 3.32539566824587, 2.7669478423497, 3.05635689537043, 3.06619073720255,
## 3.67071548348627, 2.74727091425549, 2.90087199253003, 3.1684242813721,
## 3.15700042115011, 2.85819285953193, 2.64688376586472, 3.22763733053677,
## 2.7033726115511, 3.15955035878339, 2.91506437048654, 2.98669152890184,
## 2.83790818836042, 2.96165829322024, 3.35933317756346, 2.84897089215859,
## 3.51333488159901, 3.29805662274264, 2.96424160646262, 3.09421922026864,
## 2.94180393152844, 3.0837431508767, 2.78562833574758, 3.01504458458636,
## 2.8225686545448, 3.04404613383254, 2.8541687092322, 2.65042108826557,
## 2.99473177322041, 2.88144312715186, 2.71997877196748, 3.28091121578765,
## 2.64048488160644, 2.90032208874933, 2.93225985059842, 2.91235066461494,
## 3.03302805829769, 2.57413778351594, 2.99373027088332, 2.93863268151342,
## 2.98214032003452, 2.94968833505258, 2.77383794164021, 2.85991255041146,
## 2.62321826558551, 2.58550584834412, 2.51365606307399, 2.89811944468699,
## 2.89977188240808, 2.9391619220656, 2.99021709286588, 3.17220341666977,
## 2.92369907065416, 2.89922137317315, 3.19826487096408, 2.76127496233951,
## 2.54238908520136, 2.62756295018952, 2.95021175825218, 2.75302356674494,
## 2.59301339111385, 2.37211115564266, 2.92316158071916, 2.82435065679837,
## 2.6447553507299, 2.93757335938046, 2.9391619220656, 2.83321334405622,
## 2.78377591163035, 2.97858611471902, 2.58926666511224, 3.06851794327964,
## 2.7219531062712, 2.85070650150373, 2.55567572067621, 3.03061667540749,
## 3.08557297755378, 2.74148497718845, 2.96269241947579, 2.98870765861703,
## 2.69327491552006, 2.94549105711724, 3.04452243772342, 2.65535241210176,
## 3.06479180948549, 2.86391369893314, 3.18924101973851, 2.80578168959555,
## 2.82375700881418, 2.70537997254633, 3.07639017657145, 2.68852753461335,
## 2.9391619220656, 2.69056488676119, 2.70537997254633, 2.83732253680635,
## 2.95595140354215, 2.85991255041146, 3.24804620216798, 2.6440448711263,
## 2.92262380173335, 2.78562833574758, 2.74019465442878, 2.90799335924598,
## 2.89425310460414, 3.07130346040107, 2.90635446240277, 3.08099211750481,
## 3.28952066443753, 2.84781214347737, 3.08648663682246, 3.14802408389625,
## 2.58097411853423, 2.71469474382088, 2.85359250639287, 2.77695417974942,
## 2.77695417974942, 3.00667221359233, 3.33967652501391, 2.71800053195538,
## 2.93545134266906, 2.56186769092413, 2.86105737022739, 2.61885462229774,
## 3.14802408389625, 2.7408400239252, 3.14458322028635, 2.50307395374345,
## 2.82375700881418, 2.99423114742772, 3.10368941505908, 2.87469394517693,
## 2.84374591655611, 2.93863268151342, 2.85991255041146, 2.69665215614984,
## 2.84839168565528, 2.38967979984498, 2.78315767358902, 2.7047112998367,
## 2.92262380173335, 2.69867303928961, 3.06198806933106, 3.02819946369149,
## 2.88591740754678, 2.86619290219901, 2.82316300820271, 3.07639017657145,
## 3.09602999486936, 3.39484390768998, 3.05258508514677, 3.04832472367316,
## 2.49897390699944, 2.94654202936322, 2.63762773680566, 2.77383794164021,
## 2.95125778345216, 2.95073490762326, 3.05776766447344, 2.70671597808907,
## 2.8106067894273, 2.87186828633161, 3.11484775444415, 2.8724340572095,
## 2.97246364661464, 2.82967768922391, 2.97654945413722, 2.97246364661464,
## 2.77133794033813, 2.97552956623647, 2.75110969056266, 2.84490938381941,
## 2.75937682826755, 3.2144661163795, 3.3332753651767, 2.87130219517581,
## 2.96217549002515, 3.02140002030257, 3.06991167172824, 3.2188758248682,
## 3.3403852422654, 3.42491390827947, 3.37724616083964, 3.22882615572137,
## 3.2240623515555, 3.30137704637994, 2.91017438519234, 2.90251989183181,
## 3.0022112396517, 3.03206420280138, 2.89591193827178, 3.14974008603334,
## 2.90032208874933, 2.91723004539903, 2.7033726115511, 3.40019688132857,
## 2.74855214441154, 2.75556971707019, 3.02188723103084, 2.8106067894273,
## 2.68033636253469, 2.97092715463502, 2.89203703721523, 2.95699144523756,
## 2.64333388638252, 2.42303124606991, 2.797890905102, 2.82435065679837,
## 2.93492013415723, 3.00568260440716, 3.1108450806545, 2.58248697812686,
## 3.02237420450041, 3.00617753141553, 2.86334308550825, 3.05588619637374,
## 2.79239134953596, 2.9871959425317, 2.55489902160804, 2.57566101305646,
## 2.8402473707136, 3.17596832385692, 2.68716699018579, 2.68784749378469,
## 3.02140002030257, 2.61447185414264, 2.94811641961233, 2.92369907065416,
## 3.0243197304059, 3.00864849882054, 2.90251989183181, 2.81540871942271,
## 2.63188884013665, 3.07269331469012, 2.9871959425317, 2.9274534328007,
## 2.50715725872282, 2.57794151575519, 2.598235335095, 2.79300390698237,
## 2.86903462050803, 3.03783344957263, 3.19622113430339, 3.23828621838802,
## 3.23632273847192, 2.67000213346468, 3.21847574484686, 3.23553626576131,
## 3.03013370027132, 2.79422789734326, 2.96217549002515, 3.1108450806545,
## 3.38201456224538, 3.08831145484708, 3.36453339729056, 3.32790958589232,
## 3.12148347885955, 3.17513290192028, 3.30137704637994, 3.37997374521053,
## 3.42165339022954, 3.22246936037833, 3.10861443061066, 3.34109345759245,
## 3.33505757915761, 3.37861088298936, 3.20030443928277, 2.33988087773774,
## 2.87751164216656, 3.05635689537043, 3.01455402779458, 2.66305283517147,
## 2.99473177322041, 3.03639425527288, 3.17971910966701, 3.14587493198371,
## 3.21084365317094, 3.11839228628988, 3.31563949330051, 3.0022112396517,
## 3.02916704964023, 3.09783749649114, 2.52091708731103, 2.65745841498615,
## 3.13723183582769, 3.0624559055969, 2.79728133483015, 3.06944731137627,
## 3.00815479355255, 3.2296179214001, 2.71137799119488, 3.22326617316949,
## 3.17722014959937, 2.88368276974537, 3.07223024452672, 3.07823349506573,
## 2.91343703082716, 3.22684399451738, 3.03591406318682, 3.07176695982999,
## 3.21124679770371, 3.00963517872298, 3.08236858021354, 2.86789890204411,
## 2.82375700881418, 2.68307421503203, 3.07269331469012, 2.79361608943186,
## 2.90361698464619, 2.92852352386054, 3.09195113129453, 2.92154737536461,
## 3.07223024452672, 2.46725171454928, 2.70001802940495, 3.04356960296815,
## 2.62900699376176, 3.17136484219715, 3.17555070012983, 3.04499851485691,
## 2.94654202936322, 2.85243910372751, 2.80275413657151, 3.05917644611053,
## 2.68375750853317, 3.19948911106801, 2.75937682826755, 2.80457176809283,
## 2.97807733831527, 2.78192004966867, 3.17680304844629, 3.04309284491383,
## 2.7638002162067, 3.21526932927409, 3.26918863874179, 2.75047091698616,
## 2.91885122921803, 3.06619073720255, 3.08190996979504, 2.7239235502585,
## 3.17888681665184, 3.12500460925813, 2.90690105984738, 2.9871959425317,
## 2.88144312715186, 2.99272776453369, 2.55256529826182, 2.98416563718253,
## 3.21807550469743, 2.59749101053515, 3.02140002030257, 2.96527306606928,
## 2.95958682691764, 2.74470351875025, 2.90853906185161, 2.97909463240097,
## 2.83262493568384, 3.03302805829769, 2.97807733831527, 3.00518743232475,
## 2.76190687389292, 2.75747508442973, 3.1315734964654, 2.38139627341834,
## 2.8402473707136, 3.00568260440716, 2.38784493694487, 2.79667139275574,
## 3.20639830335709, 2.93969088267037, 2.79667139275574, 3.2236643416,
## 2.58701187272515, 3.06991167172824, 2.63404478779171, 3.11218108619724,
## 2.73371794785079, 2.86619290219901, 2.48240351956988, 2.89314568477889,
## 2.85128436918812, 2.76757618041624, 2.70604819843154, 2.80819714970715,
## 2.93225985059842, 2.71997877196748, 2.88535921607262, 3.03399098567108,
## 3.03013370027132, 2.73046379593911, 2.57108434602905, 2.73046379593911,
## 2.88703285663065, 2.96836107675786, 2.54474665014402, 2.56186769092413,
## 3.00469201492546, 2.76883167336207, 2.89867056071086, 3.10099278421148,
## 2.98365969231972, 2.27315628230323, 2.9338568698359, 3.20599319903719,
## 2.83026783382646, 2.51688969564105, 2.97705900828837, 2.47569771070269,
## 2.68852753461335, 2.71800053195538, 2.67069441455844, 2.89369954798884,
## 3.10099278421148, 2.56955412384829, 3.08511583468868, 3.27978275977172,
## 2.70270259477561, 3.10950728781284, 2.71535677628465, 2.92208573338569,
## 2.84432781939476, 2.85589532836619, 2.76631910922619, 3.14069804380418,
## 3.06385810260159, 2.90251989183181, 3.14458322028635, 3.10413814739778,
## 3.0837431508767, 3.11307076597122, 3.00667221359233, 2.97348666460667,
## 2.96114082878437, 3.28353933819392, 3.16758253048065, 2.92316158071916,
## 2.84897089215859, 3.00864849882054, 3.11529150861163, 2.55800220485855,
## 2.94127608775793, 2.91614779421115, 3.24102862950933, 3.17010566049877,
## 2.82908719614504, 2.86105737022739, 3.0708397460408, 3.48031658611475,
## 2.57718192589717, 2.63188884013665, 3.00815479355255, 2.83438912314523,
## 2.60046499042227, 2.74148497718845, 3.17680304844629, 3.10593106585207,
## 2.94864066602014, 3.29879544804407, 3.52075661671979, 3.32539566824587,
## 2.7669478423497, 3.05635689537043, 3.29472513715164, 3.06619073720255,
## 3.32683296637329, 2.74727091425549, 2.71071331852169, 2.90087199253003,
## 3.15700042115011, 2.98870765861703, 2.64688376586472, 2.7033726115511,
## 2.91506437048654, 2.98669152890184, 2.96165829322024, 2.83615020372953,
## 3.35933317756346, 2.84897089215859, 3.14415227867226, 3.51333488159901,
## 3.29805662274264, 3.13809951484091, 3.09693415406296, 2.96424160646262,
## 3.09421922026864, 3.43785069931019, 2.94180393152844, 3.0837431508767,
## 2.78562833574758, 3.01504458458636, 2.8225686545448, 2.56802155649851,
## 3.04404613383254, 2.75174805636793, 3.19785645764413, 2.8541687092322,
## 2.65042108826557, 2.99473177322041, 2.88144312715186, 2.71997877196748,
## 2.64048488160644, 2.90032208874933, 2.93225985059842, 2.91235066461494,
## 3.03302805829769, 2.57413778351594, 2.99373027088332, 2.93863268151342,
## 2.98214032003452, 2.94968833505258, 2.77383794164021, 2.85991255041146,
## 2.62321826558551, 2.51365606307399, 2.89977188240808, 3.1393996233664,
## 2.99021709286588, 3.17220341666977, 2.92369907065416, 2.89922137317315,
## 3.19826487096408, 2.66722820658195, 2.54238908520136, 2.62756295018952,
## 2.95021175825218, 2.75302356674494, 2.37211115564266, 2.92316158071916,
## 2.82435065679837, 2.93757335938046, 2.9391619220656, 2.83321334405622,
## 2.78377591163035, 2.97858611471902, 2.58926666511224, 3.06851794327964,
## 2.7219531062712, 2.88647528761704, 3.03061667540749, 3.08557297755378,
## 2.74148497718845, 2.96269241947579, 2.98870765861703, 2.69327491552006,
## 2.94549105711724, 3.06479180948549, 2.86391369893314, 3.18924101973851,
## 2.80578168959555, 2.82375700881418, 2.70537997254633, 2.73760900334375,
## 2.68852753461335, 2.9391619220656, 2.77446196662146, 2.83732253680635,
## 2.95595140354215, 2.6440448711263, 2.92262380173335, 2.78562833574758,
## 2.90799335924598, 2.89425310460414, 3.07130346040107, 2.90635446240277,
## 2.83026783382646, 3.08099211750481, 2.89148225218019, 2.84781214347737,
## 3.08648663682246, 3.14802408389625, 2.71469474382088, 2.85359250639287,
## 2.77695417974942, 2.77695417974942, 3.00667221359233, 3.33967652501391,
## 2.71800053195538, 2.93545134266906, 2.7033726115511, 3.12324559385295,
## 2.61885462229774, 3.14802408389625, 2.78253905309295, 2.7408400239252,
## 3.14458322028635, 2.50307395374345, 2.82375700881418, 2.99423114742772,
## 3.10368941505908, 2.87469394517693, 2.84374591655611, 2.93863268151342,
## 2.69665215614984, 2.84839168565528, 3.04547436544881, 2.38967979984498,
## 2.90635446240277, 2.92262380173335, 3.06198806933106, 3.02819946369149,
## 2.88591740754678, 2.82316300820271, 3.07639017657145, 3.09602999486936,
## 3.39484390768998, 3.07731226054641, 2.49897390699944, 3.06385810260159,
## 2.94654202936322, 2.63762773680566, 2.77383794164021, 2.95073490762326,
## 3.05776766447344, 2.70671597808907, 3.09013294897548, 2.8106067894273,
## 2.87186828633161, 3.11484775444415, 2.8724340572095, 2.97246364661464,
## 3.08967788639652, 2.82967768922391, 2.97654945413722, 2.97246364661464,
## 2.77133794033813, 2.97552956623647, 2.75110969056266, 3.23553626576131,
## 2.75937682826755, 2.90799335924598, 2.82435065679837, 3.3332753651767,
## 2.87130219517581, 2.96217549002515, 3.02140002030257, 3.06991167172824,
## 3.2188758248682, 3.3403852422654, 2.84199817361195, 3.37724616083964,
## 3.22882615572137, 3.2240623515555, 3.33932197794407, 3.26842760369745,
## 3.29546642702991, 2.91017438519234, 3.0022112396517, 3.03206420280138,
## 2.89591193827178, 3.14974008603334, 2.90032208874933, 2.91723004539903,
## 3.33719205168624, 2.7033726115511, 2.74855214441154, 2.68033636253469,
## 2.97092715463502, 2.89203703721523, 2.95699144523756, 2.87016905057865,
## 2.42303124606991, 2.797890905102, 2.82435065679837, 2.93492013415723,
## 2.78315767358902, 3.00568260440716, 3.02334744058696, 2.55178617862755,
## 3.02237420450041, 3.00617753141553, 2.85128436918812, 3.05588619637374,
## 2.81780106506133, 2.9871959425317, 2.55489902160804, 2.57566101305646,
## 2.99773027621666, 2.75366071235426, 3.17596832385692, 2.68716699018579,
## 2.68784749378469, 3.02140002030257, 2.61447185414264, 2.94811641961233,
## 2.92369907065416, 3.0243197304059, 3.00864849882054, 2.90251989183181,
## 2.81540871942271, 3.07269331469012, 2.9871959425317, 2.9274534328007,
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## 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
## 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
## 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
## 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
## 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
## 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
## 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
## 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
## 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
## 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
## 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
## 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
## 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
## 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
## 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
## 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
## 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
## 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
## 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
## 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
## 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
## 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
## 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
## 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
## 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
## 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
## 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
## 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
## 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
## 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
## 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
## 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L)),
## mfinal = 100, coeflearn = "Breiman", control = list(minsplit = 0,
## minbucket = 0, cp = -1, maxcompete = 4L, maxsurrogate = 5L,
## usesurrogate = 2L, surrogatestyle = 0L, maxdepth = 6,
## xval = 0))
##
## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## mfinal maxdepth coeflearn
## 6 100 6 Breiman
##
## $obsLevels
## [1] "M" "B"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"vardep.summary")
## M B
## 340 572
## attr(,"class")
## [1] "boosting"MBS_AB_Tune$results## coeflearn maxdepth mfinal ROC Sens Spec ROCSD SensSD
## 1 Breiman 4 50 0.9506186 0.8800000 0.9335561 0.01896836 0.04405224
## 3 Breiman 5 50 0.9575898 0.8941176 0.9412265 0.01767991 0.05369829
## 5 Breiman 6 50 0.9619147 0.8988235 0.9415927 0.01476659 0.04458899
## 2 Breiman 4 100 0.9575073 0.8994118 0.9384409 0.01794364 0.04908329
## 4 Breiman 5 100 0.9631176 0.8982353 0.9412265 0.01755778 0.04931041
## 6 Breiman 6 100 0.9647554 0.9011765 0.9398352 0.01396669 0.04868513
## SpecSD
## 1 0.02758453
## 3 0.02202985
## 5 0.02460794
## 2 0.02898207
## 4 0.02160683
## 6 0.02269900(MBS_AB_Train_AUROC <- MBS_AB_Tune$results[MBS_AB_Tune$results$mfinal==MBS_AB_Tune$bestTune$mfinal &
MBS_AB_Tune$results$maxdepth==MBS_AB_Tune$bestTune$maxdepth &
MBS_AB_Tune$results$coeflearn==MBS_AB_Tune$bestTune$coeflearn,
c("ROC")])## [1] 0.9647554##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_AB_VarImp <- varImp(MBS_AB_Tune, scale = TRUE)
plot(MBS_AB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Adaptive Boosting",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_AB_Test <- data.frame(MBS_AB_Test_Observed = MA_Test$diagnosis,
MBS_AB_Test_Predicted = predict(MBS_AB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_AB_Test_ROC <- roc(response = MBS_AB_Test$MBS_AB_Test_Observed,
predictor = MBS_AB_Test$MBS_AB_Test_Predicted.M,
levels = rev(levels(MBS_AB_Test$MBS_AB_Test_Observed)))
(MBS_AB_Test_AUROC <- auc(MBS_AB_Test_ROC)[1])## [1] 0.99362841.5.2 Stochastic Gradient Boosting (MBS_GBM)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
GBM_Grid = expand.grid(n.trees = 500,
interaction.depth = c(4,5,6),
shrinkage = c(0.1,0.01,0.001),
n.minobsinnode = c(5, 10, 15))
##################################
# Running the stochastic gradient boosting model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
MBS_GBM_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "gbm",
tuneGrid = GBM_Grid,
metric = "ROC",
trControl = RKFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0003
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0005
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0003
## 120 1.2202 nan 0.0010 0.0004
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1908 nan 0.0010 0.0003
## 180 1.1771 nan 0.0010 0.0003
## 200 1.1639 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1384 nan 0.0010 0.0003
## 260 1.1261 nan 0.0010 0.0003
## 280 1.1143 nan 0.0010 0.0003
## 300 1.1027 nan 0.0010 0.0002
## 320 1.0915 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0003
## 360 1.0701 nan 0.0010 0.0002
## 380 1.0599 nan 0.0010 0.0002
## 400 1.0500 nan 0.0010 0.0002
## 420 1.0404 nan 0.0010 0.0002
## 440 1.0309 nan 0.0010 0.0002
## 460 1.0219 nan 0.0010 0.0002
## 480 1.0129 nan 0.0010 0.0002
## 500 1.0042 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0005
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0005
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0004
## 100 1.2371 nan 0.0010 0.0004
## 120 1.2217 nan 0.0010 0.0004
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1925 nan 0.0010 0.0003
## 180 1.1784 nan 0.0010 0.0003
## 200 1.1651 nan 0.0010 0.0003
## 220 1.1524 nan 0.0010 0.0003
## 240 1.1396 nan 0.0010 0.0003
## 260 1.1271 nan 0.0010 0.0003
## 280 1.1153 nan 0.0010 0.0003
## 300 1.1037 nan 0.0010 0.0003
## 320 1.0924 nan 0.0010 0.0002
## 340 1.0816 nan 0.0010 0.0002
## 360 1.0711 nan 0.0010 0.0002
## 380 1.0608 nan 0.0010 0.0002
## 400 1.0509 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0224 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
## 500 1.0047 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0003
## 140 1.2079 nan 0.0010 0.0003
## 160 1.1940 nan 0.0010 0.0003
## 180 1.1803 nan 0.0010 0.0003
## 200 1.1671 nan 0.0010 0.0003
## 220 1.1540 nan 0.0010 0.0003
## 240 1.1416 nan 0.0010 0.0003
## 260 1.1298 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0003
## 300 1.1065 nan 0.0010 0.0002
## 320 1.0956 nan 0.0010 0.0003
## 340 1.0848 nan 0.0010 0.0002
## 360 1.0744 nan 0.0010 0.0002
## 380 1.0642 nan 0.0010 0.0002
## 400 1.0542 nan 0.0010 0.0002
## 420 1.0446 nan 0.0010 0.0002
## 440 1.0353 nan 0.0010 0.0002
## 460 1.0260 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0084 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2479 nan 0.0010 0.0003
## 100 1.2310 nan 0.0010 0.0003
## 120 1.2152 nan 0.0010 0.0003
## 140 1.1995 nan 0.0010 0.0003
## 160 1.1842 nan 0.0010 0.0003
## 180 1.1696 nan 0.0010 0.0003
## 200 1.1553 nan 0.0010 0.0003
## 220 1.1416 nan 0.0010 0.0003
## 240 1.1285 nan 0.0010 0.0002
## 260 1.1155 nan 0.0010 0.0003
## 280 1.1031 nan 0.0010 0.0003
## 300 1.0909 nan 0.0010 0.0002
## 320 1.0792 nan 0.0010 0.0003
## 340 1.0676 nan 0.0010 0.0003
## 360 1.0562 nan 0.0010 0.0002
## 380 1.0456 nan 0.0010 0.0002
## 400 1.0351 nan 0.0010 0.0002
## 420 1.0249 nan 0.0010 0.0002
## 440 1.0149 nan 0.0010 0.0002
## 460 1.0054 nan 0.0010 0.0002
## 480 0.9959 nan 0.0010 0.0002
## 500 0.9867 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0004
## 120 1.2168 nan 0.0010 0.0003
## 140 1.2010 nan 0.0010 0.0003
## 160 1.1860 nan 0.0010 0.0003
## 180 1.1716 nan 0.0010 0.0003
## 200 1.1574 nan 0.0010 0.0003
## 220 1.1436 nan 0.0010 0.0003
## 240 1.1304 nan 0.0010 0.0003
## 260 1.1174 nan 0.0010 0.0003
## 280 1.1051 nan 0.0010 0.0002
## 300 1.0930 nan 0.0010 0.0003
## 320 1.0812 nan 0.0010 0.0003
## 340 1.0698 nan 0.0010 0.0002
## 360 1.0589 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0003
## 400 1.0374 nan 0.0010 0.0002
## 420 1.0270 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0073 nan 0.0010 0.0002
## 480 0.9980 nan 0.0010 0.0002
## 500 0.9890 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2492 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0003
## 120 1.2172 nan 0.0010 0.0003
## 140 1.2019 nan 0.0010 0.0004
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1727 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1454 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1195 nan 0.0010 0.0002
## 280 1.1069 nan 0.0010 0.0002
## 300 1.0950 nan 0.0010 0.0003
## 320 1.0835 nan 0.0010 0.0002
## 340 1.0719 nan 0.0010 0.0003
## 360 1.0610 nan 0.0010 0.0002
## 380 1.0503 nan 0.0010 0.0002
## 400 1.0401 nan 0.0010 0.0002
## 420 1.0298 nan 0.0010 0.0002
## 440 1.0200 nan 0.0010 0.0002
## 460 1.0102 nan 0.0010 0.0002
## 480 1.0008 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0005
## 10 1.3112 nan 0.0010 0.0005
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2634 nan 0.0010 0.0004
## 80 1.2455 nan 0.0010 0.0004
## 100 1.2280 nan 0.0010 0.0004
## 120 1.2111 nan 0.0010 0.0004
## 140 1.1948 nan 0.0010 0.0004
## 160 1.1793 nan 0.0010 0.0003
## 180 1.1643 nan 0.0010 0.0003
## 200 1.1497 nan 0.0010 0.0003
## 220 1.1354 nan 0.0010 0.0003
## 240 1.1217 nan 0.0010 0.0002
## 260 1.1082 nan 0.0010 0.0003
## 280 1.0952 nan 0.0010 0.0003
## 300 1.0828 nan 0.0010 0.0003
## 320 1.0705 nan 0.0010 0.0003
## 340 1.0587 nan 0.0010 0.0003
## 360 1.0473 nan 0.0010 0.0002
## 380 1.0361 nan 0.0010 0.0003
## 400 1.0250 nan 0.0010 0.0002
## 420 1.0142 nan 0.0010 0.0002
## 440 1.0037 nan 0.0010 0.0002
## 460 0.9937 nan 0.0010 0.0002
## 480 0.9839 nan 0.0010 0.0002
## 500 0.9742 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0005
## 60 1.2637 nan 0.0010 0.0005
## 80 1.2459 nan 0.0010 0.0004
## 100 1.2288 nan 0.0010 0.0004
## 120 1.2120 nan 0.0010 0.0004
## 140 1.1959 nan 0.0010 0.0004
## 160 1.1805 nan 0.0010 0.0003
## 180 1.1653 nan 0.0010 0.0003
## 200 1.1507 nan 0.0010 0.0003
## 220 1.1366 nan 0.0010 0.0003
## 240 1.1230 nan 0.0010 0.0003
## 260 1.1099 nan 0.0010 0.0003
## 280 1.0969 nan 0.0010 0.0003
## 300 1.0842 nan 0.0010 0.0003
## 320 1.0720 nan 0.0010 0.0003
## 340 1.0602 nan 0.0010 0.0002
## 360 1.0487 nan 0.0010 0.0002
## 380 1.0375 nan 0.0010 0.0003
## 400 1.0267 nan 0.0010 0.0002
## 420 1.0163 nan 0.0010 0.0002
## 440 1.0060 nan 0.0010 0.0002
## 460 0.9959 nan 0.0010 0.0002
## 480 0.9863 nan 0.0010 0.0002
## 500 0.9766 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0005
## 60 1.2647 nan 0.0010 0.0004
## 80 1.2471 nan 0.0010 0.0004
## 100 1.2300 nan 0.0010 0.0003
## 120 1.2137 nan 0.0010 0.0004
## 140 1.1976 nan 0.0010 0.0004
## 160 1.1825 nan 0.0010 0.0003
## 180 1.1676 nan 0.0010 0.0003
## 200 1.1533 nan 0.0010 0.0003
## 220 1.1395 nan 0.0010 0.0003
## 240 1.1261 nan 0.0010 0.0003
## 260 1.1129 nan 0.0010 0.0003
## 280 1.1001 nan 0.0010 0.0003
## 300 1.0877 nan 0.0010 0.0003
## 320 1.0758 nan 0.0010 0.0002
## 340 1.0643 nan 0.0010 0.0002
## 360 1.0531 nan 0.0010 0.0002
## 380 1.0422 nan 0.0010 0.0002
## 400 1.0315 nan 0.0010 0.0002
## 420 1.0209 nan 0.0010 0.0002
## 440 1.0106 nan 0.0010 0.0002
## 460 1.0006 nan 0.0010 0.0002
## 480 0.9910 nan 0.0010 0.0002
## 500 0.9816 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0048
## 3 1.2939 nan 0.0100 0.0039
## 4 1.2852 nan 0.0100 0.0037
## 5 1.2763 nan 0.0100 0.0038
## 6 1.2681 nan 0.0100 0.0039
## 7 1.2599 nan 0.0100 0.0039
## 8 1.2523 nan 0.0100 0.0032
## 9 1.2440 nan 0.0100 0.0037
## 10 1.2357 nan 0.0100 0.0035
## 20 1.1634 nan 0.0100 0.0029
## 40 1.0487 nan 0.0100 0.0023
## 60 0.9632 nan 0.0100 0.0015
## 80 0.8958 nan 0.0100 0.0012
## 100 0.8410 nan 0.0100 0.0010
## 120 0.7963 nan 0.0100 0.0008
## 140 0.7597 nan 0.0100 0.0005
## 160 0.7265 nan 0.0100 0.0005
## 180 0.7008 nan 0.0100 0.0003
## 200 0.6782 nan 0.0100 0.0002
## 220 0.6575 nan 0.0100 0.0003
## 240 0.6391 nan 0.0100 -0.0001
## 260 0.6229 nan 0.0100 0.0001
## 280 0.6073 nan 0.0100 -0.0001
## 300 0.5937 nan 0.0100 0.0000
## 320 0.5824 nan 0.0100 0.0001
## 340 0.5711 nan 0.0100 0.0000
## 360 0.5597 nan 0.0100 0.0000
## 380 0.5500 nan 0.0100 -0.0001
## 400 0.5398 nan 0.0100 -0.0000
## 420 0.5296 nan 0.0100 -0.0000
## 440 0.5201 nan 0.0100 0.0001
## 460 0.5116 nan 0.0100 -0.0001
## 480 0.5030 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0047
## 2 1.3022 nan 0.0100 0.0038
## 3 1.2943 nan 0.0100 0.0036
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2769 nan 0.0100 0.0043
## 6 1.2690 nan 0.0100 0.0036
## 7 1.2604 nan 0.0100 0.0039
## 8 1.2519 nan 0.0100 0.0039
## 9 1.2438 nan 0.0100 0.0038
## 10 1.2360 nan 0.0100 0.0037
## 20 1.1652 nan 0.0100 0.0032
## 40 1.0492 nan 0.0100 0.0017
## 60 0.9614 nan 0.0100 0.0017
## 80 0.8937 nan 0.0100 0.0011
## 100 0.8402 nan 0.0100 0.0007
## 120 0.7961 nan 0.0100 0.0007
## 140 0.7584 nan 0.0100 0.0004
## 160 0.7285 nan 0.0100 0.0004
## 180 0.7027 nan 0.0100 0.0001
## 200 0.6807 nan 0.0100 0.0002
## 220 0.6608 nan 0.0100 0.0002
## 240 0.6425 nan 0.0100 0.0001
## 260 0.6270 nan 0.0100 0.0001
## 280 0.6116 nan 0.0100 0.0001
## 300 0.5979 nan 0.0100 -0.0000
## 320 0.5845 nan 0.0100 0.0002
## 340 0.5735 nan 0.0100 0.0000
## 360 0.5626 nan 0.0100 0.0000
## 380 0.5523 nan 0.0100 0.0000
## 400 0.5424 nan 0.0100 -0.0000
## 420 0.5332 nan 0.0100 0.0001
## 440 0.5235 nan 0.0100 0.0000
## 460 0.5157 nan 0.0100 0.0001
## 480 0.5072 nan 0.0100 -0.0001
## 500 0.4992 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3015 nan 0.0100 0.0041
## 3 1.2933 nan 0.0100 0.0035
## 4 1.2839 nan 0.0100 0.0042
## 5 1.2754 nan 0.0100 0.0041
## 6 1.2673 nan 0.0100 0.0033
## 7 1.2596 nan 0.0100 0.0037
## 8 1.2511 nan 0.0100 0.0038
## 9 1.2434 nan 0.0100 0.0033
## 10 1.2357 nan 0.0100 0.0034
## 20 1.1644 nan 0.0100 0.0029
## 40 1.0521 nan 0.0100 0.0016
## 60 0.9660 nan 0.0100 0.0015
## 80 0.8986 nan 0.0100 0.0015
## 100 0.8443 nan 0.0100 0.0009
## 120 0.7998 nan 0.0100 0.0006
## 140 0.7647 nan 0.0100 0.0006
## 160 0.7346 nan 0.0100 0.0003
## 180 0.7098 nan 0.0100 0.0001
## 200 0.6868 nan 0.0100 0.0005
## 220 0.6672 nan 0.0100 0.0002
## 240 0.6488 nan 0.0100 0.0002
## 260 0.6319 nan 0.0100 0.0002
## 280 0.6178 nan 0.0100 0.0001
## 300 0.6045 nan 0.0100 0.0001
## 320 0.5919 nan 0.0100 0.0000
## 340 0.5802 nan 0.0100 0.0002
## 360 0.5693 nan 0.0100 0.0001
## 380 0.5600 nan 0.0100 -0.0001
## 400 0.5509 nan 0.0100 -0.0000
## 420 0.5414 nan 0.0100 0.0001
## 440 0.5323 nan 0.0100 -0.0001
## 460 0.5233 nan 0.0100 0.0000
## 480 0.5161 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2929 nan 0.0100 0.0044
## 4 1.2836 nan 0.0100 0.0042
## 5 1.2743 nan 0.0100 0.0039
## 6 1.2662 nan 0.0100 0.0035
## 7 1.2575 nan 0.0100 0.0041
## 8 1.2482 nan 0.0100 0.0043
## 9 1.2399 nan 0.0100 0.0043
## 10 1.2314 nan 0.0100 0.0039
## 20 1.1558 nan 0.0100 0.0033
## 40 1.0344 nan 0.0100 0.0020
## 60 0.9422 nan 0.0100 0.0017
## 80 0.8705 nan 0.0100 0.0013
## 100 0.8140 nan 0.0100 0.0009
## 120 0.7680 nan 0.0100 0.0005
## 140 0.7288 nan 0.0100 0.0005
## 160 0.6942 nan 0.0100 0.0003
## 180 0.6666 nan 0.0100 0.0002
## 200 0.6426 nan 0.0100 0.0003
## 220 0.6203 nan 0.0100 0.0001
## 240 0.6003 nan 0.0100 0.0001
## 260 0.5831 nan 0.0100 0.0002
## 280 0.5687 nan 0.0100 -0.0000
## 300 0.5544 nan 0.0100 0.0000
## 320 0.5414 nan 0.0100 0.0000
## 340 0.5279 nan 0.0100 -0.0000
## 360 0.5154 nan 0.0100 0.0002
## 380 0.5035 nan 0.0100 0.0001
## 400 0.4925 nan 0.0100 -0.0001
## 420 0.4822 nan 0.0100 0.0001
## 440 0.4717 nan 0.0100 -0.0002
## 460 0.4628 nan 0.0100 0.0000
## 480 0.4550 nan 0.0100 -0.0001
## 500 0.4458 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0048
## 2 1.3028 nan 0.0100 0.0044
## 3 1.2933 nan 0.0100 0.0046
## 4 1.2838 nan 0.0100 0.0043
## 5 1.2752 nan 0.0100 0.0036
## 6 1.2655 nan 0.0100 0.0044
## 7 1.2567 nan 0.0100 0.0040
## 8 1.2477 nan 0.0100 0.0042
## 9 1.2397 nan 0.0100 0.0035
## 10 1.2316 nan 0.0100 0.0041
## 20 1.1569 nan 0.0100 0.0033
## 40 1.0356 nan 0.0100 0.0023
## 60 0.9436 nan 0.0100 0.0017
## 80 0.8721 nan 0.0100 0.0013
## 100 0.8155 nan 0.0100 0.0009
## 120 0.7694 nan 0.0100 0.0007
## 140 0.7306 nan 0.0100 0.0005
## 160 0.6984 nan 0.0100 0.0004
## 180 0.6699 nan 0.0100 0.0004
## 200 0.6472 nan 0.0100 0.0002
## 220 0.6256 nan 0.0100 0.0002
## 240 0.6068 nan 0.0100 -0.0000
## 260 0.5894 nan 0.0100 -0.0000
## 280 0.5730 nan 0.0100 0.0003
## 300 0.5594 nan 0.0100 -0.0001
## 320 0.5468 nan 0.0100 0.0000
## 340 0.5346 nan 0.0100 -0.0001
## 360 0.5224 nan 0.0100 0.0000
## 380 0.5101 nan 0.0100 0.0000
## 400 0.4992 nan 0.0100 -0.0001
## 420 0.4891 nan 0.0100 0.0000
## 440 0.4794 nan 0.0100 -0.0000
## 460 0.4698 nan 0.0100 0.0001
## 480 0.4609 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3016 nan 0.0100 0.0049
## 3 1.2923 nan 0.0100 0.0045
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2745 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0038
## 7 1.2579 nan 0.0100 0.0038
## 8 1.2494 nan 0.0100 0.0038
## 9 1.2413 nan 0.0100 0.0033
## 10 1.2331 nan 0.0100 0.0037
## 20 1.1592 nan 0.0100 0.0027
## 40 1.0416 nan 0.0100 0.0022
## 60 0.9520 nan 0.0100 0.0018
## 80 0.8812 nan 0.0100 0.0011
## 100 0.8246 nan 0.0100 0.0010
## 120 0.7767 nan 0.0100 0.0009
## 140 0.7376 nan 0.0100 0.0007
## 160 0.7065 nan 0.0100 0.0004
## 180 0.6781 nan 0.0100 0.0003
## 200 0.6528 nan 0.0100 0.0004
## 220 0.6337 nan 0.0100 0.0002
## 240 0.6153 nan 0.0100 0.0001
## 260 0.5994 nan 0.0100 0.0001
## 280 0.5829 nan 0.0100 0.0001
## 300 0.5690 nan 0.0100 0.0001
## 320 0.5556 nan 0.0100 -0.0001
## 340 0.5424 nan 0.0100 0.0000
## 360 0.5312 nan 0.0100 -0.0000
## 380 0.5196 nan 0.0100 -0.0001
## 400 0.5087 nan 0.0100 0.0000
## 420 0.4985 nan 0.0100 -0.0002
## 440 0.4892 nan 0.0100 -0.0001
## 460 0.4801 nan 0.0100 -0.0001
## 480 0.4705 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3011 nan 0.0100 0.0046
## 3 1.2915 nan 0.0100 0.0043
## 4 1.2820 nan 0.0100 0.0040
## 5 1.2720 nan 0.0100 0.0045
## 6 1.2632 nan 0.0100 0.0041
## 7 1.2545 nan 0.0100 0.0040
## 8 1.2452 nan 0.0100 0.0038
## 9 1.2366 nan 0.0100 0.0041
## 10 1.2279 nan 0.0100 0.0038
## 20 1.1490 nan 0.0100 0.0030
## 40 1.0226 nan 0.0100 0.0024
## 60 0.9282 nan 0.0100 0.0016
## 80 0.8528 nan 0.0100 0.0013
## 100 0.7941 nan 0.0100 0.0007
## 120 0.7452 nan 0.0100 0.0008
## 140 0.7043 nan 0.0100 0.0006
## 160 0.6694 nan 0.0100 0.0006
## 180 0.6405 nan 0.0100 0.0002
## 200 0.6155 nan 0.0100 0.0001
## 220 0.5917 nan 0.0100 0.0003
## 240 0.5719 nan 0.0100 0.0000
## 260 0.5530 nan 0.0100 0.0000
## 280 0.5360 nan 0.0100 0.0000
## 300 0.5202 nan 0.0100 -0.0001
## 320 0.5054 nan 0.0100 0.0001
## 340 0.4907 nan 0.0100 -0.0001
## 360 0.4783 nan 0.0100 0.0000
## 380 0.4660 nan 0.0100 -0.0000
## 400 0.4545 nan 0.0100 -0.0000
## 420 0.4438 nan 0.0100 0.0001
## 440 0.4326 nan 0.0100 0.0001
## 460 0.4221 nan 0.0100 -0.0001
## 480 0.4106 nan 0.0100 -0.0002
## 500 0.4016 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0049
## 3 1.2924 nan 0.0100 0.0045
## 4 1.2832 nan 0.0100 0.0041
## 5 1.2741 nan 0.0100 0.0045
## 6 1.2655 nan 0.0100 0.0037
## 7 1.2563 nan 0.0100 0.0044
## 8 1.2469 nan 0.0100 0.0041
## 9 1.2380 nan 0.0100 0.0044
## 10 1.2293 nan 0.0100 0.0036
## 20 1.1520 nan 0.0100 0.0031
## 40 1.0261 nan 0.0100 0.0024
## 60 0.9310 nan 0.0100 0.0018
## 80 0.8580 nan 0.0100 0.0014
## 100 0.7988 nan 0.0100 0.0010
## 120 0.7502 nan 0.0100 0.0006
## 140 0.7092 nan 0.0100 0.0006
## 160 0.6761 nan 0.0100 0.0003
## 180 0.6467 nan 0.0100 0.0001
## 200 0.6212 nan 0.0100 0.0003
## 220 0.5982 nan 0.0100 0.0001
## 240 0.5767 nan 0.0100 0.0003
## 260 0.5586 nan 0.0100 0.0000
## 280 0.5403 nan 0.0100 0.0001
## 300 0.5238 nan 0.0100 0.0001
## 320 0.5094 nan 0.0100 0.0001
## 340 0.4965 nan 0.0100 0.0000
## 360 0.4830 nan 0.0100 -0.0001
## 380 0.4703 nan 0.0100 -0.0001
## 400 0.4593 nan 0.0100 -0.0000
## 420 0.4490 nan 0.0100 -0.0001
## 440 0.4372 nan 0.0100 0.0000
## 460 0.4280 nan 0.0100 -0.0001
## 480 0.4192 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0047
## 2 1.3017 nan 0.0100 0.0043
## 3 1.2921 nan 0.0100 0.0046
## 4 1.2828 nan 0.0100 0.0043
## 5 1.2743 nan 0.0100 0.0040
## 6 1.2655 nan 0.0100 0.0041
## 7 1.2573 nan 0.0100 0.0038
## 8 1.2482 nan 0.0100 0.0041
## 9 1.2401 nan 0.0100 0.0037
## 10 1.2310 nan 0.0100 0.0044
## 20 1.1524 nan 0.0100 0.0035
## 40 1.0299 nan 0.0100 0.0023
## 60 0.9371 nan 0.0100 0.0017
## 80 0.8650 nan 0.0100 0.0013
## 100 0.8070 nan 0.0100 0.0009
## 120 0.7593 nan 0.0100 0.0005
## 140 0.7193 nan 0.0100 0.0004
## 160 0.6870 nan 0.0100 0.0003
## 180 0.6578 nan 0.0100 0.0005
## 200 0.6327 nan 0.0100 0.0003
## 220 0.6093 nan 0.0100 0.0005
## 240 0.5898 nan 0.0100 0.0000
## 260 0.5718 nan 0.0100 0.0002
## 280 0.5550 nan 0.0100 0.0002
## 300 0.5395 nan 0.0100 0.0000
## 320 0.5254 nan 0.0100 -0.0000
## 340 0.5120 nan 0.0100 -0.0001
## 360 0.4996 nan 0.0100 0.0001
## 380 0.4875 nan 0.0100 0.0000
## 400 0.4755 nan 0.0100 -0.0001
## 420 0.4645 nan 0.0100 -0.0000
## 440 0.4539 nan 0.0100 -0.0001
## 460 0.4441 nan 0.0100 -0.0000
## 480 0.4343 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2395 nan 0.1000 0.0380
## 2 1.1673 nan 0.1000 0.0351
## 3 1.0980 nan 0.1000 0.0309
## 4 1.0441 nan 0.1000 0.0253
## 5 0.9992 nan 0.1000 0.0217
## 6 0.9546 nan 0.1000 0.0190
## 7 0.9201 nan 0.1000 0.0151
## 8 0.8905 nan 0.1000 0.0106
## 9 0.8656 nan 0.1000 0.0109
## 10 0.8411 nan 0.1000 0.0100
## 20 0.6848 nan 0.1000 0.0001
## 40 0.5461 nan 0.1000 0.0011
## 60 0.4675 nan 0.1000 -0.0009
## 80 0.4098 nan 0.1000 -0.0008
## 100 0.3573 nan 0.1000 0.0000
## 120 0.3145 nan 0.1000 -0.0011
## 140 0.2825 nan 0.1000 -0.0011
## 160 0.2582 nan 0.1000 -0.0009
## 180 0.2287 nan 0.1000 -0.0007
## 200 0.2035 nan 0.1000 -0.0002
## 220 0.1852 nan 0.1000 0.0002
## 240 0.1650 nan 0.1000 0.0001
## 260 0.1474 nan 0.1000 -0.0001
## 280 0.1345 nan 0.1000 -0.0003
## 300 0.1217 nan 0.1000 -0.0004
## 320 0.1091 nan 0.1000 -0.0002
## 340 0.0997 nan 0.1000 -0.0001
## 360 0.0902 nan 0.1000 -0.0000
## 380 0.0831 nan 0.1000 -0.0002
## 400 0.0771 nan 0.1000 -0.0002
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## 440 0.0649 nan 0.1000 -0.0003
## 460 0.0593 nan 0.1000 -0.0002
## 480 0.0540 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2274 nan 0.1000 0.0435
## 2 1.1525 nan 0.1000 0.0338
## 3 1.0924 nan 0.1000 0.0262
## 4 1.0394 nan 0.1000 0.0218
## 5 0.9977 nan 0.1000 0.0180
## 6 0.9542 nan 0.1000 0.0183
## 7 0.9212 nan 0.1000 0.0142
## 8 0.8916 nan 0.1000 0.0113
## 9 0.8647 nan 0.1000 0.0106
## 10 0.8432 nan 0.1000 0.0076
## 20 0.6875 nan 0.1000 0.0029
## 40 0.5502 nan 0.1000 0.0004
## 60 0.4753 nan 0.1000 -0.0005
## 80 0.4122 nan 0.1000 0.0005
## 100 0.3649 nan 0.1000 -0.0008
## 120 0.3219 nan 0.1000 -0.0005
## 140 0.2866 nan 0.1000 0.0001
## 160 0.2570 nan 0.1000 -0.0008
## 180 0.2315 nan 0.1000 -0.0003
## 200 0.2066 nan 0.1000 -0.0007
## 220 0.1851 nan 0.1000 -0.0002
## 240 0.1686 nan 0.1000 -0.0006
## 260 0.1519 nan 0.1000 -0.0001
## 280 0.1368 nan 0.1000 -0.0001
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## 320 0.1165 nan 0.1000 -0.0002
## 340 0.1059 nan 0.1000 -0.0004
## 360 0.0962 nan 0.1000 -0.0001
## 380 0.0882 nan 0.1000 -0.0002
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## 480 0.0562 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0419
## 2 1.1719 nan 0.1000 0.0275
## 3 1.1045 nan 0.1000 0.0277
## 4 1.0521 nan 0.1000 0.0203
## 5 1.0015 nan 0.1000 0.0235
## 6 0.9621 nan 0.1000 0.0163
## 7 0.9296 nan 0.1000 0.0137
## 8 0.8979 nan 0.1000 0.0104
## 9 0.8701 nan 0.1000 0.0122
## 10 0.8481 nan 0.1000 0.0078
## 20 0.6911 nan 0.1000 0.0025
## 40 0.5637 nan 0.1000 -0.0013
## 60 0.4836 nan 0.1000 -0.0013
## 80 0.4253 nan 0.1000 -0.0008
## 100 0.3737 nan 0.1000 -0.0012
## 120 0.3345 nan 0.1000 -0.0005
## 140 0.2953 nan 0.1000 -0.0002
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## 180 0.2402 nan 0.1000 -0.0003
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## 220 0.1931 nan 0.1000 0.0001
## 240 0.1763 nan 0.1000 -0.0012
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## 280 0.1467 nan 0.1000 -0.0001
## 300 0.1344 nan 0.1000 -0.0004
## 320 0.1240 nan 0.1000 -0.0006
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## 380 0.0966 nan 0.1000 -0.0004
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## 460 0.0702 nan 0.1000 -0.0002
## 480 0.0650 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2292 nan 0.1000 0.0396
## 2 1.1522 nan 0.1000 0.0357
## 3 1.0877 nan 0.1000 0.0292
## 4 1.0292 nan 0.1000 0.0264
## 5 0.9815 nan 0.1000 0.0199
## 6 0.9429 nan 0.1000 0.0153
## 7 0.9058 nan 0.1000 0.0167
## 8 0.8730 nan 0.1000 0.0122
## 9 0.8455 nan 0.1000 0.0094
## 10 0.8191 nan 0.1000 0.0112
## 20 0.6497 nan 0.1000 0.0010
## 40 0.5048 nan 0.1000 0.0003
## 60 0.4124 nan 0.1000 -0.0006
## 80 0.3461 nan 0.1000 -0.0004
## 100 0.2924 nan 0.1000 -0.0006
## 120 0.2489 nan 0.1000 0.0001
## 140 0.2147 nan 0.1000 -0.0008
## 160 0.1885 nan 0.1000 -0.0004
## 180 0.1653 nan 0.1000 0.0001
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## 220 0.1308 nan 0.1000 -0.0002
## 240 0.1145 nan 0.1000 -0.0003
## 260 0.1010 nan 0.1000 -0.0000
## 280 0.0896 nan 0.1000 -0.0000
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## 320 0.0716 nan 0.1000 -0.0000
## 340 0.0638 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2337 nan 0.1000 0.0425
## 2 1.1574 nan 0.1000 0.0374
## 3 1.0907 nan 0.1000 0.0288
## 4 1.0341 nan 0.1000 0.0262
## 5 0.9834 nan 0.1000 0.0197
## 6 0.9392 nan 0.1000 0.0185
## 7 0.9062 nan 0.1000 0.0128
## 8 0.8750 nan 0.1000 0.0114
## 9 0.8426 nan 0.1000 0.0113
## 10 0.8163 nan 0.1000 0.0075
## 20 0.6550 nan 0.1000 0.0022
## 40 0.5157 nan 0.1000 -0.0008
## 60 0.4271 nan 0.1000 -0.0006
## 80 0.3599 nan 0.1000 -0.0003
## 100 0.3088 nan 0.1000 -0.0013
## 120 0.2661 nan 0.1000 -0.0004
## 140 0.2263 nan 0.1000 -0.0006
## 160 0.1980 nan 0.1000 0.0003
## 180 0.1737 nan 0.1000 -0.0004
## 200 0.1524 nan 0.1000 -0.0008
## 220 0.1335 nan 0.1000 -0.0003
## 240 0.1188 nan 0.1000 -0.0003
## 260 0.1062 nan 0.1000 -0.0002
## 280 0.0956 nan 0.1000 -0.0005
## 300 0.0849 nan 0.1000 -0.0000
## 320 0.0756 nan 0.1000 -0.0002
## 340 0.0685 nan 0.1000 -0.0005
## 360 0.0607 nan 0.1000 -0.0003
## 380 0.0537 nan 0.1000 -0.0000
## 400 0.0483 nan 0.1000 -0.0003
## 420 0.0428 nan 0.1000 -0.0000
## 440 0.0386 nan 0.1000 -0.0001
## 460 0.0342 nan 0.1000 0.0000
## 480 0.0305 nan 0.1000 -0.0001
## 500 0.0275 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0401
## 2 1.1540 nan 0.1000 0.0344
## 3 1.0894 nan 0.1000 0.0299
## 4 1.0348 nan 0.1000 0.0241
## 5 0.9838 nan 0.1000 0.0205
## 6 0.9462 nan 0.1000 0.0163
## 7 0.9081 nan 0.1000 0.0149
## 8 0.8754 nan 0.1000 0.0144
## 9 0.8447 nan 0.1000 0.0118
## 10 0.8205 nan 0.1000 0.0082
## 20 0.6599 nan 0.1000 0.0009
## 40 0.5170 nan 0.1000 0.0007
## 60 0.4304 nan 0.1000 -0.0005
## 80 0.3627 nan 0.1000 -0.0005
## 100 0.3120 nan 0.1000 -0.0011
## 120 0.2710 nan 0.1000 -0.0011
## 140 0.2360 nan 0.1000 -0.0007
## 160 0.2071 nan 0.1000 -0.0006
## 180 0.1850 nan 0.1000 -0.0009
## 200 0.1616 nan 0.1000 -0.0004
## 220 0.1429 nan 0.1000 -0.0006
## 240 0.1260 nan 0.1000 -0.0007
## 260 0.1116 nan 0.1000 -0.0003
## 280 0.0999 nan 0.1000 -0.0000
## 300 0.0886 nan 0.1000 -0.0005
## 320 0.0794 nan 0.1000 -0.0002
## 340 0.0716 nan 0.1000 -0.0002
## 360 0.0641 nan 0.1000 -0.0002
## 380 0.0577 nan 0.1000 -0.0001
## 400 0.0514 nan 0.1000 -0.0001
## 420 0.0459 nan 0.1000 -0.0003
## 440 0.0408 nan 0.1000 -0.0001
## 460 0.0372 nan 0.1000 -0.0002
## 480 0.0333 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2293 nan 0.1000 0.0461
## 2 1.1488 nan 0.1000 0.0350
## 3 1.0820 nan 0.1000 0.0293
## 4 1.0263 nan 0.1000 0.0224
## 5 0.9736 nan 0.1000 0.0245
## 6 0.9291 nan 0.1000 0.0200
## 7 0.8939 nan 0.1000 0.0143
## 8 0.8558 nan 0.1000 0.0154
## 9 0.8232 nan 0.1000 0.0118
## 10 0.7971 nan 0.1000 0.0084
## 20 0.6222 nan 0.1000 0.0035
## 40 0.4590 nan 0.1000 -0.0004
## 60 0.3666 nan 0.1000 -0.0003
## 80 0.3002 nan 0.1000 0.0002
## 100 0.2490 nan 0.1000 -0.0003
## 120 0.2071 nan 0.1000 -0.0002
## 140 0.1737 nan 0.1000 -0.0003
## 160 0.1480 nan 0.1000 -0.0007
## 180 0.1278 nan 0.1000 -0.0002
## 200 0.1088 nan 0.1000 -0.0002
## 220 0.0939 nan 0.1000 -0.0002
## 240 0.0828 nan 0.1000 -0.0003
## 260 0.0734 nan 0.1000 -0.0003
## 280 0.0644 nan 0.1000 -0.0002
## 300 0.0564 nan 0.1000 -0.0001
## 320 0.0494 nan 0.1000 -0.0000
## 340 0.0438 nan 0.1000 -0.0000
## 360 0.0382 nan 0.1000 -0.0001
## 380 0.0337 nan 0.1000 -0.0001
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## 480 0.0176 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0466
## 2 1.1447 nan 0.1000 0.0390
## 3 1.0775 nan 0.1000 0.0298
## 4 1.0187 nan 0.1000 0.0242
## 5 0.9669 nan 0.1000 0.0225
## 6 0.9271 nan 0.1000 0.0169
## 7 0.8912 nan 0.1000 0.0138
## 8 0.8590 nan 0.1000 0.0123
## 9 0.8215 nan 0.1000 0.0135
## 10 0.7916 nan 0.1000 0.0098
## 20 0.6172 nan 0.1000 0.0032
## 40 0.4670 nan 0.1000 0.0000
## 60 0.3744 nan 0.1000 -0.0015
## 80 0.2980 nan 0.1000 -0.0004
## 100 0.2519 nan 0.1000 -0.0008
## 120 0.2099 nan 0.1000 -0.0009
## 140 0.1771 nan 0.1000 -0.0006
## 160 0.1490 nan 0.1000 -0.0008
## 180 0.1271 nan 0.1000 -0.0003
## 200 0.1090 nan 0.1000 -0.0006
## 220 0.0946 nan 0.1000 -0.0003
## 240 0.0811 nan 0.1000 -0.0002
## 260 0.0709 nan 0.1000 -0.0002
## 280 0.0620 nan 0.1000 -0.0003
## 300 0.0541 nan 0.1000 -0.0001
## 320 0.0475 nan 0.1000 -0.0001
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## 380 0.0327 nan 0.1000 -0.0001
## 400 0.0288 nan 0.1000 -0.0001
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## 460 0.0195 nan 0.1000 -0.0001
## 480 0.0170 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2275 nan 0.1000 0.0399
## 2 1.1516 nan 0.1000 0.0331
## 3 1.0902 nan 0.1000 0.0257
## 4 1.0345 nan 0.1000 0.0246
## 5 0.9809 nan 0.1000 0.0215
## 6 0.9368 nan 0.1000 0.0194
## 7 0.9011 nan 0.1000 0.0153
## 8 0.8678 nan 0.1000 0.0146
## 9 0.8378 nan 0.1000 0.0118
## 10 0.8092 nan 0.1000 0.0112
## 20 0.6364 nan 0.1000 0.0039
## 40 0.4870 nan 0.1000 0.0010
## 60 0.3891 nan 0.1000 0.0000
## 80 0.3211 nan 0.1000 -0.0016
## 100 0.2677 nan 0.1000 -0.0004
## 120 0.2288 nan 0.1000 -0.0010
## 140 0.1946 nan 0.1000 -0.0009
## 160 0.1654 nan 0.1000 -0.0003
## 180 0.1434 nan 0.1000 -0.0004
## 200 0.1256 nan 0.1000 -0.0003
## 220 0.1088 nan 0.1000 -0.0006
## 240 0.0959 nan 0.1000 -0.0006
## 260 0.0825 nan 0.1000 -0.0003
## 280 0.0723 nan 0.1000 -0.0003
## 300 0.0629 nan 0.1000 -0.0002
## 320 0.0561 nan 0.1000 -0.0004
## 340 0.0494 nan 0.1000 -0.0004
## 360 0.0426 nan 0.1000 -0.0001
## 380 0.0380 nan 0.1000 -0.0002
## 400 0.0333 nan 0.1000 -0.0002
## 420 0.0295 nan 0.1000 -0.0000
## 440 0.0257 nan 0.1000 -0.0000
## 460 0.0230 nan 0.1000 -0.0001
## 480 0.0204 nan 0.1000 -0.0000
## 500 0.0179 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0003
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0003
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2887 nan 0.0010 0.0004
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2586 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0003
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1669 nan 0.0010 0.0003
## 240 1.1555 nan 0.0010 0.0002
## 260 1.1443 nan 0.0010 0.0002
## 280 1.1335 nan 0.0010 0.0002
## 300 1.1227 nan 0.0010 0.0002
## 320 1.1125 nan 0.0010 0.0002
## 340 1.1026 nan 0.0010 0.0002
## 360 1.0926 nan 0.0010 0.0002
## 380 1.0833 nan 0.0010 0.0002
## 400 1.0739 nan 0.0010 0.0002
## 420 1.0651 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0477 nan 0.0010 0.0002
## 480 1.0392 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0003
## 60 1.2740 nan 0.0010 0.0003
## 80 1.2593 nan 0.0010 0.0003
## 100 1.2451 nan 0.0010 0.0003
## 120 1.2315 nan 0.0010 0.0003
## 140 1.2181 nan 0.0010 0.0003
## 160 1.2053 nan 0.0010 0.0003
## 180 1.1930 nan 0.0010 0.0003
## 200 1.1808 nan 0.0010 0.0003
## 220 1.1688 nan 0.0010 0.0003
## 240 1.1575 nan 0.0010 0.0002
## 260 1.1460 nan 0.0010 0.0002
## 280 1.1352 nan 0.0010 0.0002
## 300 1.1246 nan 0.0010 0.0002
## 320 1.1140 nan 0.0010 0.0003
## 340 1.1038 nan 0.0010 0.0002
## 360 1.0939 nan 0.0010 0.0002
## 380 1.0843 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0658 nan 0.0010 0.0002
## 440 1.0570 nan 0.0010 0.0002
## 460 1.0482 nan 0.0010 0.0002
## 480 1.0399 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0004
## 9 1.3139 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2893 nan 0.0010 0.0003
## 60 1.2742 nan 0.0010 0.0004
## 80 1.2595 nan 0.0010 0.0003
## 100 1.2454 nan 0.0010 0.0004
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2182 nan 0.0010 0.0003
## 160 1.2055 nan 0.0010 0.0002
## 180 1.1930 nan 0.0010 0.0002
## 200 1.1808 nan 0.0010 0.0002
## 220 1.1690 nan 0.0010 0.0002
## 240 1.1574 nan 0.0010 0.0003
## 260 1.1460 nan 0.0010 0.0003
## 280 1.1348 nan 0.0010 0.0002
## 300 1.1242 nan 0.0010 0.0002
## 320 1.1138 nan 0.0010 0.0002
## 340 1.1037 nan 0.0010 0.0002
## 360 1.0940 nan 0.0010 0.0002
## 380 1.0844 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0660 nan 0.0010 0.0002
## 440 1.0573 nan 0.0010 0.0002
## 460 1.0488 nan 0.0010 0.0002
## 480 1.0402 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2705 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2398 nan 0.0010 0.0004
## 120 1.2246 nan 0.0010 0.0003
## 140 1.2102 nan 0.0010 0.0003
## 160 1.1964 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1573 nan 0.0010 0.0002
## 240 1.1448 nan 0.0010 0.0002
## 260 1.1328 nan 0.0010 0.0002
## 280 1.1210 nan 0.0010 0.0003
## 300 1.1095 nan 0.0010 0.0003
## 320 1.0986 nan 0.0010 0.0002
## 340 1.0879 nan 0.0010 0.0002
## 360 1.0776 nan 0.0010 0.0002
## 380 1.0676 nan 0.0010 0.0002
## 400 1.0578 nan 0.0010 0.0002
## 420 1.0483 nan 0.0010 0.0002
## 440 1.0389 nan 0.0010 0.0002
## 460 1.0298 nan 0.0010 0.0002
## 480 1.0208 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2397 nan 0.0010 0.0003
## 120 1.2253 nan 0.0010 0.0003
## 140 1.2110 nan 0.0010 0.0003
## 160 1.1973 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1709 nan 0.0010 0.0003
## 220 1.1586 nan 0.0010 0.0003
## 240 1.1464 nan 0.0010 0.0003
## 260 1.1345 nan 0.0010 0.0002
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1115 nan 0.0010 0.0003
## 320 1.1006 nan 0.0010 0.0002
## 340 1.0898 nan 0.0010 0.0002
## 360 1.0796 nan 0.0010 0.0002
## 380 1.0696 nan 0.0010 0.0002
## 400 1.0598 nan 0.0010 0.0002
## 420 1.0503 nan 0.0010 0.0002
## 440 1.0411 nan 0.0010 0.0002
## 460 1.0318 nan 0.0010 0.0002
## 480 1.0227 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0004
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1990 nan 0.0010 0.0003
## 180 1.1860 nan 0.0010 0.0003
## 200 1.1730 nan 0.0010 0.0003
## 220 1.1606 nan 0.0010 0.0003
## 240 1.1483 nan 0.0010 0.0003
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## 280 1.1243 nan 0.0010 0.0003
## 300 1.1133 nan 0.0010 0.0002
## 320 1.1026 nan 0.0010 0.0002
## 340 1.0922 nan 0.0010 0.0002
## 360 1.0821 nan 0.0010 0.0002
## 380 1.0721 nan 0.0010 0.0002
## 400 1.0623 nan 0.0010 0.0002
## 420 1.0529 nan 0.0010 0.0002
## 440 1.0438 nan 0.0010 0.0002
## 460 1.0348 nan 0.0010 0.0002
## 480 1.0260 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0005
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2519 nan 0.0010 0.0003
## 100 1.2359 nan 0.0010 0.0004
## 120 1.2206 nan 0.0010 0.0003
## 140 1.2058 nan 0.0010 0.0003
## 160 1.1914 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0002
## 200 1.1638 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1382 nan 0.0010 0.0003
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## 300 1.1017 nan 0.0010 0.0002
## 320 1.0903 nan 0.0010 0.0003
## 340 1.0790 nan 0.0010 0.0002
## 360 1.0682 nan 0.0010 0.0002
## 380 1.0575 nan 0.0010 0.0002
## 400 1.0471 nan 0.0010 0.0002
## 420 1.0371 nan 0.0010 0.0002
## 440 1.0272 nan 0.0010 0.0002
## 460 1.0177 nan 0.0010 0.0002
## 480 1.0086 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0005
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2521 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1911 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0003
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## 320 1.0902 nan 0.0010 0.0003
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## 380 1.0583 nan 0.0010 0.0002
## 400 1.0480 nan 0.0010 0.0002
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## 480 1.0098 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0004
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## 140 1.2079 nan 0.0010 0.0003
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## 380 1.0617 nan 0.0010 0.0002
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## 480 1.0135 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0036
## 3 1.2965 nan 0.0100 0.0034
## 4 1.2892 nan 0.0100 0.0033
## 5 1.2812 nan 0.0100 0.0038
## 6 1.2743 nan 0.0100 0.0028
## 7 1.2668 nan 0.0100 0.0034
## 8 1.2593 nan 0.0100 0.0037
## 9 1.2515 nan 0.0100 0.0031
## 10 1.2439 nan 0.0100 0.0031
## 20 1.1793 nan 0.0100 0.0026
## 40 1.0729 nan 0.0100 0.0020
## 60 0.9926 nan 0.0100 0.0013
## 80 0.9294 nan 0.0100 0.0011
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## 180 0.7434 nan 0.0100 0.0003
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## 320 0.6281 nan 0.0100 0.0000
## 340 0.6162 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0038
## 4 1.2884 nan 0.0100 0.0036
## 5 1.2812 nan 0.0100 0.0033
## 6 1.2734 nan 0.0100 0.0032
## 7 1.2661 nan 0.0100 0.0030
## 8 1.2590 nan 0.0100 0.0035
## 9 1.2517 nan 0.0100 0.0031
## 10 1.2443 nan 0.0100 0.0029
## 20 1.1763 nan 0.0100 0.0026
## 40 1.0713 nan 0.0100 0.0019
## 60 0.9912 nan 0.0100 0.0016
## 80 0.9280 nan 0.0100 0.0011
## 100 0.8773 nan 0.0100 0.0007
## 120 0.8346 nan 0.0100 0.0008
## 140 0.8007 nan 0.0100 0.0006
## 160 0.7701 nan 0.0100 0.0005
## 180 0.7458 nan 0.0100 0.0003
## 200 0.7244 nan 0.0100 0.0003
## 220 0.7053 nan 0.0100 0.0003
## 240 0.6886 nan 0.0100 0.0002
## 260 0.6731 nan 0.0100 0.0001
## 280 0.6578 nan 0.0100 0.0001
## 300 0.6439 nan 0.0100 -0.0000
## 320 0.6316 nan 0.0100 0.0001
## 340 0.6208 nan 0.0100 -0.0001
## 360 0.6100 nan 0.0100 0.0001
## 380 0.5991 nan 0.0100 -0.0002
## 400 0.5895 nan 0.0100 -0.0000
## 420 0.5799 nan 0.0100 0.0001
## 440 0.5709 nan 0.0100 0.0000
## 460 0.5620 nan 0.0100 0.0001
## 480 0.5536 nan 0.0100 -0.0002
## 500 0.5447 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0038
## 2 1.3048 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0040
## 4 1.2876 nan 0.0100 0.0036
## 5 1.2795 nan 0.0100 0.0037
## 6 1.2724 nan 0.0100 0.0032
## 7 1.2654 nan 0.0100 0.0031
## 8 1.2585 nan 0.0100 0.0029
## 9 1.2518 nan 0.0100 0.0030
## 10 1.2447 nan 0.0100 0.0032
## 20 1.1785 nan 0.0100 0.0028
## 40 1.0727 nan 0.0100 0.0016
## 60 0.9918 nan 0.0100 0.0016
## 80 0.9299 nan 0.0100 0.0011
## 100 0.8798 nan 0.0100 0.0007
## 120 0.8375 nan 0.0100 0.0009
## 140 0.8014 nan 0.0100 0.0005
## 160 0.7725 nan 0.0100 0.0003
## 180 0.7469 nan 0.0100 0.0001
## 200 0.7267 nan 0.0100 0.0002
## 220 0.7077 nan 0.0100 0.0002
## 240 0.6918 nan 0.0100 0.0000
## 260 0.6776 nan 0.0100 0.0003
## 280 0.6637 nan 0.0100 0.0001
## 300 0.6506 nan 0.0100 -0.0000
## 320 0.6389 nan 0.0100 -0.0001
## 340 0.6271 nan 0.0100 -0.0000
## 360 0.6162 nan 0.0100 0.0001
## 380 0.6064 nan 0.0100 -0.0001
## 400 0.5973 nan 0.0100 0.0000
## 420 0.5881 nan 0.0100 -0.0000
## 440 0.5795 nan 0.0100 -0.0001
## 460 0.5706 nan 0.0100 -0.0001
## 480 0.5626 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2946 nan 0.0100 0.0035
## 4 1.2860 nan 0.0100 0.0035
## 5 1.2779 nan 0.0100 0.0038
## 6 1.2696 nan 0.0100 0.0034
## 7 1.2614 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0031
## 9 1.2463 nan 0.0100 0.0033
## 10 1.2391 nan 0.0100 0.0031
## 20 1.1704 nan 0.0100 0.0030
## 40 1.0566 nan 0.0100 0.0020
## 60 0.9691 nan 0.0100 0.0018
## 80 0.9043 nan 0.0100 0.0013
## 100 0.8500 nan 0.0100 0.0009
## 120 0.8071 nan 0.0100 0.0005
## 140 0.7700 nan 0.0100 0.0006
## 160 0.7387 nan 0.0100 0.0004
## 180 0.7122 nan 0.0100 0.0004
## 200 0.6879 nan 0.0100 0.0001
## 220 0.6667 nan 0.0100 0.0002
## 240 0.6473 nan 0.0100 0.0002
## 260 0.6304 nan 0.0100 0.0001
## 280 0.6150 nan 0.0100 -0.0003
## 300 0.5998 nan 0.0100 0.0001
## 320 0.5858 nan 0.0100 0.0000
## 340 0.5728 nan 0.0100 0.0001
## 360 0.5601 nan 0.0100 0.0000
## 380 0.5474 nan 0.0100 -0.0000
## 400 0.5365 nan 0.0100 0.0002
## 420 0.5258 nan 0.0100 0.0001
## 440 0.5154 nan 0.0100 0.0000
## 460 0.5062 nan 0.0100 -0.0001
## 480 0.4972 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2941 nan 0.0100 0.0036
## 4 1.2861 nan 0.0100 0.0035
## 5 1.2779 nan 0.0100 0.0036
## 6 1.2696 nan 0.0100 0.0037
## 7 1.2620 nan 0.0100 0.0034
## 8 1.2542 nan 0.0100 0.0031
## 9 1.2464 nan 0.0100 0.0035
## 10 1.2391 nan 0.0100 0.0033
## 20 1.1709 nan 0.0100 0.0029
## 40 1.0586 nan 0.0100 0.0022
## 60 0.9755 nan 0.0100 0.0014
## 80 0.9072 nan 0.0100 0.0007
## 100 0.8542 nan 0.0100 0.0011
## 120 0.8098 nan 0.0100 0.0006
## 140 0.7729 nan 0.0100 0.0008
## 160 0.7424 nan 0.0100 0.0003
## 180 0.7162 nan 0.0100 0.0004
## 200 0.6923 nan 0.0100 -0.0002
## 220 0.6716 nan 0.0100 0.0002
## 240 0.6537 nan 0.0100 0.0002
## 260 0.6367 nan 0.0100 0.0001
## 280 0.6223 nan 0.0100 0.0001
## 300 0.6075 nan 0.0100 0.0001
## 320 0.5929 nan 0.0100 -0.0000
## 340 0.5792 nan 0.0100 0.0000
## 360 0.5670 nan 0.0100 -0.0001
## 380 0.5552 nan 0.0100 -0.0001
## 400 0.5440 nan 0.0100 -0.0001
## 420 0.5326 nan 0.0100 0.0003
## 440 0.5232 nan 0.0100 -0.0002
## 460 0.5128 nan 0.0100 -0.0000
## 480 0.5031 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3041 nan 0.0100 0.0034
## 3 1.2954 nan 0.0100 0.0035
## 4 1.2876 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0040
## 6 1.2715 nan 0.0100 0.0031
## 7 1.2636 nan 0.0100 0.0035
## 8 1.2556 nan 0.0100 0.0033
## 9 1.2484 nan 0.0100 0.0031
## 10 1.2411 nan 0.0100 0.0029
## 20 1.1723 nan 0.0100 0.0029
## 40 1.0607 nan 0.0100 0.0020
## 60 0.9751 nan 0.0100 0.0015
## 80 0.9086 nan 0.0100 0.0012
## 100 0.8548 nan 0.0100 0.0006
## 120 0.8123 nan 0.0100 0.0007
## 140 0.7765 nan 0.0100 0.0006
## 160 0.7453 nan 0.0100 0.0005
## 180 0.7199 nan 0.0100 0.0003
## 200 0.6964 nan 0.0100 0.0002
## 220 0.6753 nan 0.0100 0.0001
## 240 0.6564 nan 0.0100 0.0002
## 260 0.6409 nan 0.0100 -0.0001
## 280 0.6243 nan 0.0100 0.0001
## 300 0.6105 nan 0.0100 0.0001
## 320 0.5969 nan 0.0100 0.0001
## 340 0.5860 nan 0.0100 0.0001
## 360 0.5749 nan 0.0100 0.0000
## 380 0.5630 nan 0.0100 0.0001
## 400 0.5519 nan 0.0100 0.0001
## 420 0.5413 nan 0.0100 0.0002
## 440 0.5314 nan 0.0100 0.0001
## 460 0.5212 nan 0.0100 -0.0001
## 480 0.5121 nan 0.0100 -0.0000
## 500 0.5022 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0041
## 2 1.3028 nan 0.0100 0.0036
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2848 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0039
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2591 nan 0.0100 0.0035
## 8 1.2511 nan 0.0100 0.0034
## 9 1.2433 nan 0.0100 0.0034
## 10 1.2353 nan 0.0100 0.0035
## 20 1.1624 nan 0.0100 0.0027
## 40 1.0462 nan 0.0100 0.0024
## 60 0.9582 nan 0.0100 0.0014
## 80 0.8870 nan 0.0100 0.0011
## 100 0.8298 nan 0.0100 0.0008
## 120 0.7828 nan 0.0100 0.0008
## 140 0.7441 nan 0.0100 0.0004
## 160 0.7102 nan 0.0100 0.0005
## 180 0.6813 nan 0.0100 0.0002
## 200 0.6558 nan 0.0100 0.0001
## 220 0.6336 nan 0.0100 0.0001
## 240 0.6123 nan 0.0100 0.0002
## 260 0.5934 nan 0.0100 0.0000
## 280 0.5764 nan 0.0100 0.0001
## 300 0.5593 nan 0.0100 0.0001
## 320 0.5443 nan 0.0100 -0.0001
## 340 0.5302 nan 0.0100 0.0001
## 360 0.5164 nan 0.0100 0.0001
## 380 0.5045 nan 0.0100 0.0001
## 400 0.4934 nan 0.0100 -0.0001
## 420 0.4817 nan 0.0100 0.0000
## 440 0.4707 nan 0.0100 0.0001
## 460 0.4609 nan 0.0100 0.0001
## 480 0.4508 nan 0.0100 -0.0000
## 500 0.4418 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3039 nan 0.0100 0.0040
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2849 nan 0.0100 0.0041
## 5 1.2759 nan 0.0100 0.0040
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2590 nan 0.0100 0.0039
## 8 1.2510 nan 0.0100 0.0033
## 9 1.2429 nan 0.0100 0.0038
## 10 1.2355 nan 0.0100 0.0032
## 20 1.1629 nan 0.0100 0.0030
## 40 1.0474 nan 0.0100 0.0021
## 60 0.9585 nan 0.0100 0.0015
## 80 0.8876 nan 0.0100 0.0011
## 100 0.8330 nan 0.0100 0.0009
## 120 0.7874 nan 0.0100 0.0007
## 140 0.7498 nan 0.0100 0.0005
## 160 0.7175 nan 0.0100 0.0004
## 180 0.6887 nan 0.0100 0.0002
## 200 0.6630 nan 0.0100 0.0002
## 220 0.6416 nan 0.0100 0.0001
## 240 0.6223 nan 0.0100 -0.0001
## 260 0.6028 nan 0.0100 0.0002
## 280 0.5860 nan 0.0100 0.0002
## 300 0.5698 nan 0.0100 0.0001
## 320 0.5552 nan 0.0100 0.0002
## 340 0.5407 nan 0.0100 0.0000
## 360 0.5279 nan 0.0100 0.0000
## 380 0.5161 nan 0.0100 -0.0001
## 400 0.5045 nan 0.0100 -0.0001
## 420 0.4925 nan 0.0100 0.0001
## 440 0.4821 nan 0.0100 -0.0002
## 460 0.4715 nan 0.0100 -0.0000
## 480 0.4617 nan 0.0100 -0.0001
## 500 0.4511 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0039
## 2 1.3036 nan 0.0100 0.0035
## 3 1.2950 nan 0.0100 0.0043
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2777 nan 0.0100 0.0039
## 6 1.2694 nan 0.0100 0.0036
## 7 1.2604 nan 0.0100 0.0040
## 8 1.2527 nan 0.0100 0.0034
## 9 1.2454 nan 0.0100 0.0032
## 10 1.2383 nan 0.0100 0.0034
## 20 1.1685 nan 0.0100 0.0025
## 40 1.0529 nan 0.0100 0.0022
## 60 0.9639 nan 0.0100 0.0016
## 80 0.8954 nan 0.0100 0.0012
## 100 0.8410 nan 0.0100 0.0009
## 120 0.7926 nan 0.0100 0.0009
## 140 0.7562 nan 0.0100 0.0008
## 160 0.7249 nan 0.0100 0.0005
## 180 0.6976 nan 0.0100 0.0003
## 200 0.6732 nan 0.0100 0.0003
## 220 0.6512 nan 0.0100 0.0000
## 240 0.6313 nan 0.0100 0.0002
## 260 0.6129 nan 0.0100 0.0000
## 280 0.5961 nan 0.0100 0.0003
## 300 0.5809 nan 0.0100 0.0001
## 320 0.5670 nan 0.0100 0.0001
## 340 0.5545 nan 0.0100 0.0000
## 360 0.5417 nan 0.0100 -0.0001
## 380 0.5293 nan 0.0100 0.0001
## 400 0.5175 nan 0.0100 -0.0002
## 420 0.5068 nan 0.0100 -0.0000
## 440 0.4966 nan 0.0100 0.0001
## 460 0.4866 nan 0.0100 0.0001
## 480 0.4770 nan 0.0100 -0.0002
## 500 0.4683 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0351
## 2 1.1721 nan 0.1000 0.0326
## 3 1.1215 nan 0.1000 0.0251
## 4 1.0710 nan 0.1000 0.0240
## 5 1.0276 nan 0.1000 0.0167
## 6 0.9915 nan 0.1000 0.0154
## 7 0.9602 nan 0.1000 0.0142
## 8 0.9274 nan 0.1000 0.0128
## 9 0.9027 nan 0.1000 0.0100
## 10 0.8817 nan 0.1000 0.0068
## 20 0.7310 nan 0.1000 0.0004
## 40 0.5952 nan 0.1000 0.0018
## 60 0.5108 nan 0.1000 0.0006
## 80 0.4454 nan 0.1000 -0.0002
## 100 0.3963 nan 0.1000 -0.0004
## 120 0.3522 nan 0.1000 0.0001
## 140 0.3156 nan 0.1000 -0.0002
## 160 0.2874 nan 0.1000 -0.0013
## 180 0.2574 nan 0.1000 -0.0007
## 200 0.2339 nan 0.1000 -0.0008
## 220 0.2108 nan 0.1000 -0.0005
## 240 0.1919 nan 0.1000 -0.0000
## 260 0.1750 nan 0.1000 -0.0001
## 280 0.1591 nan 0.1000 -0.0000
## 300 0.1461 nan 0.1000 -0.0005
## 320 0.1349 nan 0.1000 -0.0000
## 340 0.1243 nan 0.1000 -0.0000
## 360 0.1135 nan 0.1000 -0.0001
## 380 0.1045 nan 0.1000 -0.0004
## 400 0.0972 nan 0.1000 -0.0001
## 420 0.0892 nan 0.1000 -0.0002
## 440 0.0822 nan 0.1000 -0.0002
## 460 0.0754 nan 0.1000 -0.0001
## 480 0.0692 nan 0.1000 -0.0000
## 500 0.0642 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2456 nan 0.1000 0.0342
## 2 1.1787 nan 0.1000 0.0306
## 3 1.1188 nan 0.1000 0.0276
## 4 1.0739 nan 0.1000 0.0175
## 5 1.0296 nan 0.1000 0.0192
## 6 0.9899 nan 0.1000 0.0164
## 7 0.9560 nan 0.1000 0.0141
## 8 0.9268 nan 0.1000 0.0111
## 9 0.8987 nan 0.1000 0.0129
## 10 0.8775 nan 0.1000 0.0080
## 20 0.7242 nan 0.1000 0.0008
## 40 0.5901 nan 0.1000 0.0015
## 60 0.5084 nan 0.1000 -0.0002
## 80 0.4478 nan 0.1000 -0.0008
## 100 0.3931 nan 0.1000 -0.0015
## 120 0.3543 nan 0.1000 -0.0001
## 140 0.3128 nan 0.1000 -0.0008
## 160 0.2826 nan 0.1000 -0.0003
## 180 0.2567 nan 0.1000 -0.0009
## 200 0.2328 nan 0.1000 0.0002
## 220 0.2134 nan 0.1000 -0.0001
## 240 0.1934 nan 0.1000 -0.0003
## 260 0.1782 nan 0.1000 -0.0004
## 280 0.1611 nan 0.1000 -0.0003
## 300 0.1487 nan 0.1000 -0.0001
## 320 0.1345 nan 0.1000 -0.0002
## 340 0.1233 nan 0.1000 -0.0005
## 360 0.1132 nan 0.1000 -0.0005
## 380 0.1034 nan 0.1000 -0.0003
## 400 0.0948 nan 0.1000 -0.0002
## 420 0.0865 nan 0.1000 -0.0001
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## 460 0.0746 nan 0.1000 -0.0002
## 480 0.0691 nan 0.1000 -0.0002
## 500 0.0641 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0338
## 2 1.1772 nan 0.1000 0.0289
## 3 1.1188 nan 0.1000 0.0242
## 4 1.0690 nan 0.1000 0.0201
## 5 1.0274 nan 0.1000 0.0172
## 6 0.9891 nan 0.1000 0.0146
## 7 0.9611 nan 0.1000 0.0108
## 8 0.9276 nan 0.1000 0.0133
## 9 0.8999 nan 0.1000 0.0107
## 10 0.8758 nan 0.1000 0.0086
## 20 0.7266 nan 0.1000 0.0034
## 40 0.5911 nan 0.1000 -0.0003
## 60 0.5180 nan 0.1000 -0.0023
## 80 0.4554 nan 0.1000 0.0001
## 100 0.4065 nan 0.1000 -0.0014
## 120 0.3608 nan 0.1000 -0.0010
## 140 0.3246 nan 0.1000 -0.0003
## 160 0.2956 nan 0.1000 -0.0011
## 180 0.2648 nan 0.1000 -0.0017
## 200 0.2383 nan 0.1000 -0.0011
## 220 0.2173 nan 0.1000 -0.0005
## 240 0.1999 nan 0.1000 -0.0012
## 260 0.1847 nan 0.1000 -0.0003
## 280 0.1688 nan 0.1000 0.0001
## 300 0.1544 nan 0.1000 -0.0009
## 320 0.1421 nan 0.1000 -0.0005
## 340 0.1323 nan 0.1000 -0.0006
## 360 0.1228 nan 0.1000 -0.0001
## 380 0.1149 nan 0.1000 -0.0004
## 400 0.1070 nan 0.1000 -0.0006
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## 460 0.0839 nan 0.1000 -0.0003
## 480 0.0772 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0391
## 2 1.1663 nan 0.1000 0.0317
## 3 1.1084 nan 0.1000 0.0233
## 4 1.0592 nan 0.1000 0.0195
## 5 1.0149 nan 0.1000 0.0164
## 6 0.9768 nan 0.1000 0.0161
## 7 0.9421 nan 0.1000 0.0130
## 8 0.9090 nan 0.1000 0.0120
## 9 0.8806 nan 0.1000 0.0087
## 10 0.8567 nan 0.1000 0.0070
## 20 0.6846 nan 0.1000 0.0036
## 40 0.5441 nan 0.1000 0.0007
## 60 0.4508 nan 0.1000 0.0007
## 80 0.3771 nan 0.1000 -0.0012
## 100 0.3274 nan 0.1000 0.0008
## 120 0.2859 nan 0.1000 -0.0002
## 140 0.2516 nan 0.1000 -0.0010
## 160 0.2189 nan 0.1000 -0.0010
## 180 0.1943 nan 0.1000 -0.0005
## 200 0.1727 nan 0.1000 -0.0003
## 220 0.1519 nan 0.1000 0.0000
## 240 0.1345 nan 0.1000 -0.0001
## 260 0.1211 nan 0.1000 -0.0004
## 280 0.1083 nan 0.1000 -0.0002
## 300 0.0967 nan 0.1000 -0.0001
## 320 0.0867 nan 0.1000 -0.0001
## 340 0.0781 nan 0.1000 -0.0005
## 360 0.0709 nan 0.1000 -0.0004
## 380 0.0640 nan 0.1000 -0.0001
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## 480 0.0392 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0364
## 2 1.1677 nan 0.1000 0.0318
## 3 1.1084 nan 0.1000 0.0275
## 4 1.0541 nan 0.1000 0.0215
## 5 1.0095 nan 0.1000 0.0181
## 6 0.9688 nan 0.1000 0.0145
## 7 0.9325 nan 0.1000 0.0170
## 8 0.8998 nan 0.1000 0.0152
## 9 0.8723 nan 0.1000 0.0089
## 10 0.8443 nan 0.1000 0.0117
## 20 0.6886 nan 0.1000 0.0006
## 40 0.5434 nan 0.1000 -0.0000
## 60 0.4573 nan 0.1000 -0.0001
## 80 0.3887 nan 0.1000 -0.0003
## 100 0.3354 nan 0.1000 -0.0005
## 120 0.2908 nan 0.1000 -0.0007
## 140 0.2574 nan 0.1000 -0.0008
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## 180 0.1987 nan 0.1000 -0.0010
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## 240 0.1358 nan 0.1000 0.0001
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## 280 0.1085 nan 0.1000 -0.0004
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## 320 0.0890 nan 0.1000 -0.0006
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## 380 0.0661 nan 0.1000 -0.0002
## 400 0.0600 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0407
## 2 1.1613 nan 0.1000 0.0310
## 3 1.1051 nan 0.1000 0.0272
## 4 1.0503 nan 0.1000 0.0237
## 5 1.0038 nan 0.1000 0.0191
## 6 0.9637 nan 0.1000 0.0145
## 7 0.9318 nan 0.1000 0.0140
## 8 0.8990 nan 0.1000 0.0148
## 9 0.8759 nan 0.1000 0.0076
## 10 0.8523 nan 0.1000 0.0094
## 20 0.7075 nan 0.1000 0.0007
## 40 0.5638 nan 0.1000 -0.0001
## 60 0.4784 nan 0.1000 0.0004
## 80 0.4104 nan 0.1000 -0.0011
## 100 0.3529 nan 0.1000 -0.0009
## 120 0.3083 nan 0.1000 -0.0006
## 140 0.2743 nan 0.1000 -0.0012
## 160 0.2436 nan 0.1000 -0.0010
## 180 0.2199 nan 0.1000 -0.0000
## 200 0.1953 nan 0.1000 -0.0011
## 220 0.1744 nan 0.1000 -0.0003
## 240 0.1573 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2383 nan 0.1000 0.0387
## 2 1.1579 nan 0.1000 0.0360
## 3 1.0965 nan 0.1000 0.0285
## 4 1.0450 nan 0.1000 0.0221
## 5 0.9962 nan 0.1000 0.0205
## 6 0.9571 nan 0.1000 0.0152
## 7 0.9173 nan 0.1000 0.0166
## 8 0.8795 nan 0.1000 0.0130
## 9 0.8487 nan 0.1000 0.0117
## 10 0.8193 nan 0.1000 0.0100
## 20 0.6546 nan 0.1000 0.0034
## 40 0.4944 nan 0.1000 0.0020
## 60 0.3997 nan 0.1000 -0.0004
## 80 0.3364 nan 0.1000 -0.0002
## 100 0.2802 nan 0.1000 -0.0005
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## 140 0.2057 nan 0.1000 -0.0011
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## 180 0.1527 nan 0.1000 -0.0009
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## 240 0.1005 nan 0.1000 -0.0001
## 260 0.0892 nan 0.1000 -0.0001
## 280 0.0786 nan 0.1000 -0.0001
## 300 0.0702 nan 0.1000 -0.0002
## 320 0.0610 nan 0.1000 -0.0000
## 340 0.0542 nan 0.1000 -0.0001
## 360 0.0478 nan 0.1000 -0.0001
## 380 0.0420 nan 0.1000 -0.0001
## 400 0.0372 nan 0.1000 -0.0002
## 420 0.0331 nan 0.1000 -0.0001
## 440 0.0296 nan 0.1000 0.0000
## 460 0.0264 nan 0.1000 -0.0000
## 480 0.0234 nan 0.1000 0.0000
## 500 0.0211 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0367
## 2 1.1644 nan 0.1000 0.0303
## 3 1.0983 nan 0.1000 0.0302
## 4 1.0389 nan 0.1000 0.0248
## 5 0.9880 nan 0.1000 0.0219
## 6 0.9463 nan 0.1000 0.0163
## 7 0.9142 nan 0.1000 0.0131
## 8 0.8792 nan 0.1000 0.0135
## 9 0.8531 nan 0.1000 0.0093
## 10 0.8281 nan 0.1000 0.0081
## 20 0.6629 nan 0.1000 0.0016
## 40 0.5090 nan 0.1000 -0.0013
## 60 0.4115 nan 0.1000 -0.0011
## 80 0.3411 nan 0.1000 -0.0022
## 100 0.2831 nan 0.1000 -0.0011
## 120 0.2416 nan 0.1000 -0.0016
## 140 0.2082 nan 0.1000 -0.0007
## 160 0.1770 nan 0.1000 -0.0006
## 180 0.1530 nan 0.1000 -0.0008
## 200 0.1337 nan 0.1000 -0.0004
## 220 0.1144 nan 0.1000 -0.0001
## 240 0.1003 nan 0.1000 -0.0004
## 260 0.0876 nan 0.1000 0.0001
## 280 0.0773 nan 0.1000 -0.0003
## 300 0.0686 nan 0.1000 -0.0004
## 320 0.0612 nan 0.1000 -0.0003
## 340 0.0537 nan 0.1000 -0.0001
## 360 0.0475 nan 0.1000 -0.0002
## 380 0.0419 nan 0.1000 -0.0002
## 400 0.0371 nan 0.1000 -0.0002
## 420 0.0327 nan 0.1000 -0.0001
## 440 0.0291 nan 0.1000 -0.0001
## 460 0.0259 nan 0.1000 -0.0001
## 480 0.0231 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2416 nan 0.1000 0.0364
## 2 1.1654 nan 0.1000 0.0367
## 3 1.0994 nan 0.1000 0.0257
## 4 1.0472 nan 0.1000 0.0212
## 5 1.0013 nan 0.1000 0.0192
## 6 0.9615 nan 0.1000 0.0153
## 7 0.9210 nan 0.1000 0.0178
## 8 0.8936 nan 0.1000 0.0112
## 9 0.8646 nan 0.1000 0.0127
## 10 0.8405 nan 0.1000 0.0097
## 20 0.6775 nan 0.1000 0.0012
## 40 0.5218 nan 0.1000 0.0004
## 60 0.4265 nan 0.1000 -0.0006
## 80 0.3591 nan 0.1000 -0.0014
## 100 0.3051 nan 0.1000 -0.0010
## 120 0.2610 nan 0.1000 -0.0019
## 140 0.2205 nan 0.1000 -0.0015
## 160 0.1930 nan 0.1000 -0.0007
## 180 0.1687 nan 0.1000 -0.0006
## 200 0.1450 nan 0.1000 -0.0004
## 220 0.1280 nan 0.1000 -0.0002
## 240 0.1122 nan 0.1000 -0.0006
## 260 0.1001 nan 0.1000 -0.0002
## 280 0.0879 nan 0.1000 -0.0004
## 300 0.0778 nan 0.1000 -0.0002
## 320 0.0686 nan 0.1000 -0.0004
## 340 0.0604 nan 0.1000 -0.0004
## 360 0.0542 nan 0.1000 -0.0003
## 380 0.0482 nan 0.1000 -0.0003
## 400 0.0428 nan 0.1000 -0.0002
## 420 0.0384 nan 0.1000 -0.0002
## 440 0.0343 nan 0.1000 -0.0001
## 460 0.0312 nan 0.1000 -0.0001
## 480 0.0276 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0003
## 100 1.2405 nan 0.0010 0.0004
## 120 1.2263 nan 0.0010 0.0002
## 140 1.2125 nan 0.0010 0.0003
## 160 1.1990 nan 0.0010 0.0003
## 180 1.1861 nan 0.0010 0.0003
## 200 1.1732 nan 0.0010 0.0002
## 220 1.1611 nan 0.0010 0.0002
## 240 1.1491 nan 0.0010 0.0002
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## 320 1.1045 nan 0.0010 0.0002
## 340 1.0940 nan 0.0010 0.0002
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## 380 1.0742 nan 0.0010 0.0002
## 400 1.0646 nan 0.0010 0.0002
## 420 1.0552 nan 0.0010 0.0002
## 440 1.0461 nan 0.0010 0.0002
## 460 1.0374 nan 0.0010 0.0002
## 480 1.0288 nan 0.0010 0.0002
## 500 1.0202 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0003
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0003
## 80 1.2552 nan 0.0010 0.0004
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2258 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
## 160 1.1981 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1597 nan 0.0010 0.0003
## 240 1.1480 nan 0.0010 0.0002
## 260 1.1363 nan 0.0010 0.0003
## 280 1.1251 nan 0.0010 0.0002
## 300 1.1142 nan 0.0010 0.0002
## 320 1.1038 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0837 nan 0.0010 0.0002
## 380 1.0739 nan 0.0010 0.0002
## 400 1.0645 nan 0.0010 0.0002
## 420 1.0553 nan 0.0010 0.0002
## 440 1.0463 nan 0.0010 0.0002
## 460 1.0376 nan 0.0010 0.0002
## 480 1.0290 nan 0.0010 0.0002
## 500 1.0208 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0003
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0003
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2712 nan 0.0010 0.0004
## 80 1.2559 nan 0.0010 0.0003
## 100 1.2408 nan 0.0010 0.0003
## 120 1.2264 nan 0.0010 0.0003
## 140 1.2126 nan 0.0010 0.0003
## 160 1.1992 nan 0.0010 0.0002
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1733 nan 0.0010 0.0003
## 220 1.1612 nan 0.0010 0.0003
## 240 1.1494 nan 0.0010 0.0003
## 260 1.1381 nan 0.0010 0.0003
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1165 nan 0.0010 0.0003
## 320 1.1060 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0859 nan 0.0010 0.0002
## 380 1.0763 nan 0.0010 0.0002
## 400 1.0671 nan 0.0010 0.0002
## 420 1.0581 nan 0.0010 0.0002
## 440 1.0495 nan 0.0010 0.0002
## 460 1.0407 nan 0.0010 0.0002
## 480 1.0323 nan 0.0010 0.0002
## 500 1.0243 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0003
## 100 1.2359 nan 0.0010 0.0003
## 120 1.2205 nan 0.0010 0.0003
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0003
## 200 1.1642 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
## 260 1.1265 nan 0.0010 0.0002
## 280 1.1149 nan 0.0010 0.0003
## 300 1.1034 nan 0.0010 0.0003
## 320 1.0921 nan 0.0010 0.0003
## 340 1.0811 nan 0.0010 0.0002
## 360 1.0706 nan 0.0010 0.0002
## 380 1.0602 nan 0.0010 0.0003
## 400 1.0503 nan 0.0010 0.0002
## 420 1.0405 nan 0.0010 0.0002
## 440 1.0308 nan 0.0010 0.0002
## 460 1.0213 nan 0.0010 0.0002
## 480 1.0125 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0004
## 100 1.2364 nan 0.0010 0.0003
## 120 1.2213 nan 0.0010 0.0003
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1926 nan 0.0010 0.0003
## 180 1.1787 nan 0.0010 0.0003
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1526 nan 0.0010 0.0003
## 240 1.1399 nan 0.0010 0.0003
## 260 1.1279 nan 0.0010 0.0003
## 280 1.1160 nan 0.0010 0.0002
## 300 1.1047 nan 0.0010 0.0002
## 320 1.0937 nan 0.0010 0.0002
## 340 1.0829 nan 0.0010 0.0002
## 360 1.0726 nan 0.0010 0.0002
## 380 1.0625 nan 0.0010 0.0002
## 400 1.0526 nan 0.0010 0.0002
## 420 1.0429 nan 0.0010 0.0002
## 440 1.0333 nan 0.0010 0.0002
## 460 1.0242 nan 0.0010 0.0002
## 480 1.0153 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0005
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2377 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0004
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1678 nan 0.0010 0.0003
## 220 1.1549 nan 0.0010 0.0002
## 240 1.1428 nan 0.0010 0.0002
## 260 1.1306 nan 0.0010 0.0003
## 280 1.1188 nan 0.0010 0.0003
## 300 1.1075 nan 0.0010 0.0003
## 320 1.0964 nan 0.0010 0.0002
## 340 1.0858 nan 0.0010 0.0002
## 360 1.0754 nan 0.0010 0.0002
## 380 1.0652 nan 0.0010 0.0002
## 400 1.0554 nan 0.0010 0.0002
## 420 1.0456 nan 0.0010 0.0002
## 440 1.0365 nan 0.0010 0.0002
## 460 1.0275 nan 0.0010 0.0002
## 480 1.0185 nan 0.0010 0.0002
## 500 1.0097 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3016 nan 0.0010 0.0005
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2656 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0003
## 100 1.2320 nan 0.0010 0.0003
## 120 1.2161 nan 0.0010 0.0003
## 140 1.2006 nan 0.0010 0.0004
## 160 1.1857 nan 0.0010 0.0003
## 180 1.1714 nan 0.0010 0.0003
## 200 1.1577 nan 0.0010 0.0002
## 220 1.1444 nan 0.0010 0.0003
## 240 1.1314 nan 0.0010 0.0003
## 260 1.1188 nan 0.0010 0.0003
## 280 1.1065 nan 0.0010 0.0003
## 300 1.0947 nan 0.0010 0.0003
## 320 1.0830 nan 0.0010 0.0003
## 340 1.0718 nan 0.0010 0.0002
## 360 1.0609 nan 0.0010 0.0002
## 380 1.0501 nan 0.0010 0.0002
## 400 1.0397 nan 0.0010 0.0002
## 420 1.0297 nan 0.0010 0.0002
## 440 1.0197 nan 0.0010 0.0002
## 460 1.0101 nan 0.0010 0.0002
## 480 1.0008 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2332 nan 0.0010 0.0004
## 120 1.2172 nan 0.0010 0.0003
## 140 1.2021 nan 0.0010 0.0003
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1452 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1195 nan 0.0010 0.0003
## 280 1.1073 nan 0.0010 0.0002
## 300 1.0956 nan 0.0010 0.0002
## 320 1.0843 nan 0.0010 0.0003
## 340 1.0729 nan 0.0010 0.0003
## 360 1.0618 nan 0.0010 0.0002
## 380 1.0515 nan 0.0010 0.0002
## 400 1.0412 nan 0.0010 0.0002
## 420 1.0311 nan 0.0010 0.0002
## 440 1.0215 nan 0.0010 0.0001
## 460 1.0120 nan 0.0010 0.0002
## 480 1.0029 nan 0.0010 0.0002
## 500 0.9940 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0003
## 80 1.2510 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0004
## 120 1.2197 nan 0.0010 0.0004
## 140 1.2046 nan 0.0010 0.0003
## 160 1.1899 nan 0.0010 0.0003
## 180 1.1760 nan 0.0010 0.0003
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## 220 1.1493 nan 0.0010 0.0003
## 240 1.1363 nan 0.0010 0.0003
## 260 1.1237 nan 0.0010 0.0003
## 280 1.1116 nan 0.0010 0.0002
## 300 1.0997 nan 0.0010 0.0002
## 320 1.0886 nan 0.0010 0.0003
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## 360 1.0668 nan 0.0010 0.0002
## 380 1.0564 nan 0.0010 0.0002
## 400 1.0464 nan 0.0010 0.0002
## 420 1.0366 nan 0.0010 0.0002
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## 460 1.0175 nan 0.0010 0.0002
## 480 1.0083 nan 0.0010 0.0002
## 500 0.9994 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3027 nan 0.0100 0.0039
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2864 nan 0.0100 0.0038
## 5 1.2783 nan 0.0100 0.0032
## 6 1.2699 nan 0.0100 0.0033
## 7 1.2623 nan 0.0100 0.0039
## 8 1.2551 nan 0.0100 0.0034
## 9 1.2479 nan 0.0100 0.0033
## 10 1.2400 nan 0.0100 0.0037
## 20 1.1710 nan 0.0100 0.0027
## 40 1.0651 nan 0.0100 0.0018
## 60 0.9848 nan 0.0100 0.0012
## 80 0.9193 nan 0.0100 0.0013
## 100 0.8687 nan 0.0100 0.0010
## 120 0.8263 nan 0.0100 0.0006
## 140 0.7923 nan 0.0100 0.0006
## 160 0.7643 nan 0.0100 0.0005
## 180 0.7391 nan 0.0100 0.0003
## 200 0.7170 nan 0.0100 0.0002
## 220 0.6980 nan 0.0100 -0.0000
## 240 0.6803 nan 0.0100 0.0002
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## 280 0.6508 nan 0.0100 -0.0001
## 300 0.6387 nan 0.0100 0.0000
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## 400 0.5856 nan 0.0100 -0.0001
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## 460 0.5580 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0043
## 2 1.3032 nan 0.0100 0.0038
## 3 1.2945 nan 0.0100 0.0037
## 4 1.2872 nan 0.0100 0.0032
## 5 1.2789 nan 0.0100 0.0041
## 6 1.2705 nan 0.0100 0.0038
## 7 1.2630 nan 0.0100 0.0033
## 8 1.2550 nan 0.0100 0.0034
## 9 1.2472 nan 0.0100 0.0035
## 10 1.2393 nan 0.0100 0.0033
## 20 1.1710 nan 0.0100 0.0025
## 40 1.0627 nan 0.0100 0.0020
## 60 0.9811 nan 0.0100 0.0014
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## 120 0.8230 nan 0.0100 0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0042
## 2 1.3035 nan 0.0100 0.0042
## 3 1.2954 nan 0.0100 0.0037
## 4 1.2871 nan 0.0100 0.0038
## 5 1.2793 nan 0.0100 0.0037
## 6 1.2719 nan 0.0100 0.0034
## 7 1.2641 nan 0.0100 0.0035
## 8 1.2569 nan 0.0100 0.0034
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## 20 1.1753 nan 0.0100 0.0029
## 40 1.0657 nan 0.0100 0.0022
## 60 0.9821 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0042
## 2 1.3023 nan 0.0100 0.0040
## 3 1.2930 nan 0.0100 0.0041
## 4 1.2849 nan 0.0100 0.0036
## 5 1.2762 nan 0.0100 0.0037
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2601 nan 0.0100 0.0038
## 8 1.2521 nan 0.0100 0.0037
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## 40 1.0525 nan 0.0100 0.0020
## 60 0.9674 nan 0.0100 0.0015
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## 160 0.7328 nan 0.0100 0.0003
## 180 0.7061 nan 0.0100 0.0002
## 200 0.6819 nan 0.0100 0.0003
## 220 0.6605 nan 0.0100 0.0002
## 240 0.6417 nan 0.0100 0.0003
## 260 0.6248 nan 0.0100 -0.0000
## 280 0.6089 nan 0.0100 0.0001
## 300 0.5962 nan 0.0100 -0.0002
## 320 0.5834 nan 0.0100 0.0001
## 340 0.5700 nan 0.0100 0.0001
## 360 0.5577 nan 0.0100 0.0001
## 380 0.5463 nan 0.0100 -0.0000
## 400 0.5360 nan 0.0100 0.0000
## 420 0.5256 nan 0.0100 0.0001
## 440 0.5158 nan 0.0100 -0.0001
## 460 0.5056 nan 0.0100 -0.0000
## 480 0.4970 nan 0.0100 -0.0001
## 500 0.4875 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0043
## 4 1.2854 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0041
## 6 1.2685 nan 0.0100 0.0039
## 7 1.2607 nan 0.0100 0.0031
## 8 1.2522 nan 0.0100 0.0038
## 9 1.2444 nan 0.0100 0.0035
## 10 1.2366 nan 0.0100 0.0035
## 20 1.1656 nan 0.0100 0.0030
## 40 1.0540 nan 0.0100 0.0020
## 60 0.9689 nan 0.0100 0.0018
## 80 0.9006 nan 0.0100 0.0011
## 100 0.8465 nan 0.0100 0.0010
## 120 0.8023 nan 0.0100 0.0006
## 140 0.7652 nan 0.0100 0.0004
## 160 0.7352 nan 0.0100 0.0002
## 180 0.7090 nan 0.0100 0.0003
## 200 0.6860 nan 0.0100 0.0002
## 220 0.6670 nan 0.0100 0.0001
## 240 0.6488 nan 0.0100 0.0001
## 260 0.6323 nan 0.0100 0.0001
## 280 0.6161 nan 0.0100 0.0000
## 300 0.6001 nan 0.0100 0.0001
## 320 0.5866 nan 0.0100 -0.0000
## 340 0.5739 nan 0.0100 -0.0000
## 360 0.5625 nan 0.0100 -0.0001
## 380 0.5516 nan 0.0100 -0.0001
## 400 0.5412 nan 0.0100 0.0001
## 420 0.5308 nan 0.0100 -0.0001
## 440 0.5213 nan 0.0100 0.0000
## 460 0.5118 nan 0.0100 -0.0001
## 480 0.5036 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0041
## 3 1.2940 nan 0.0100 0.0041
## 4 1.2851 nan 0.0100 0.0040
## 5 1.2767 nan 0.0100 0.0036
## 6 1.2682 nan 0.0100 0.0040
## 7 1.2596 nan 0.0100 0.0040
## 8 1.2521 nan 0.0100 0.0039
## 9 1.2444 nan 0.0100 0.0038
## 10 1.2370 nan 0.0100 0.0030
## 20 1.1678 nan 0.0100 0.0029
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9677 nan 0.0100 0.0017
## 80 0.9018 nan 0.0100 0.0012
## 100 0.8488 nan 0.0100 0.0009
## 120 0.8071 nan 0.0100 0.0006
## 140 0.7717 nan 0.0100 0.0006
## 160 0.7413 nan 0.0100 0.0006
## 180 0.7154 nan 0.0100 0.0001
## 200 0.6937 nan 0.0100 0.0001
## 220 0.6737 nan 0.0100 0.0002
## 240 0.6555 nan 0.0100 0.0002
## 260 0.6385 nan 0.0100 0.0001
## 280 0.6226 nan 0.0100 0.0001
## 300 0.6083 nan 0.0100 0.0000
## 320 0.5957 nan 0.0100 -0.0000
## 340 0.5846 nan 0.0100 0.0001
## 360 0.5731 nan 0.0100 -0.0002
## 380 0.5616 nan 0.0100 0.0000
## 400 0.5509 nan 0.0100 -0.0001
## 420 0.5404 nan 0.0100 -0.0001
## 440 0.5306 nan 0.0100 -0.0000
## 460 0.5213 nan 0.0100 -0.0001
## 480 0.5127 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0046
## 2 1.3006 nan 0.0100 0.0045
## 3 1.2916 nan 0.0100 0.0039
## 4 1.2823 nan 0.0100 0.0046
## 5 1.2732 nan 0.0100 0.0038
## 6 1.2646 nan 0.0100 0.0036
## 7 1.2564 nan 0.0100 0.0030
## 8 1.2483 nan 0.0100 0.0036
## 9 1.2405 nan 0.0100 0.0035
## 10 1.2323 nan 0.0100 0.0039
## 20 1.1577 nan 0.0100 0.0031
## 40 1.0383 nan 0.0100 0.0021
## 60 0.9479 nan 0.0100 0.0018
## 80 0.8786 nan 0.0100 0.0013
## 100 0.8227 nan 0.0100 0.0010
## 120 0.7755 nan 0.0100 0.0007
## 140 0.7375 nan 0.0100 0.0005
## 160 0.7052 nan 0.0100 0.0005
## 180 0.6785 nan 0.0100 0.0002
## 200 0.6540 nan 0.0100 0.0001
## 220 0.6311 nan 0.0100 0.0003
## 240 0.6120 nan 0.0100 -0.0001
## 260 0.5934 nan 0.0100 0.0001
## 280 0.5758 nan 0.0100 0.0002
## 300 0.5607 nan 0.0100 0.0000
## 320 0.5462 nan 0.0100 -0.0002
## 340 0.5328 nan 0.0100 0.0000
## 360 0.5196 nan 0.0100 -0.0000
## 380 0.5070 nan 0.0100 -0.0001
## 400 0.4945 nan 0.0100 -0.0001
## 420 0.4839 nan 0.0100 -0.0000
## 440 0.4738 nan 0.0100 0.0000
## 460 0.4642 nan 0.0100 0.0001
## 480 0.4544 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2926 nan 0.0100 0.0039
## 4 1.2834 nan 0.0100 0.0042
## 5 1.2748 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0036
## 7 1.2570 nan 0.0100 0.0041
## 8 1.2488 nan 0.0100 0.0037
## 9 1.2406 nan 0.0100 0.0035
## 10 1.2331 nan 0.0100 0.0036
## 20 1.1583 nan 0.0100 0.0033
## 40 1.0416 nan 0.0100 0.0022
## 60 0.9517 nan 0.0100 0.0016
## 80 0.8816 nan 0.0100 0.0011
## 100 0.8247 nan 0.0100 0.0009
## 120 0.7781 nan 0.0100 0.0006
## 140 0.7405 nan 0.0100 0.0003
## 160 0.7095 nan 0.0100 0.0004
## 180 0.6818 nan 0.0100 0.0002
## 200 0.6569 nan 0.0100 0.0002
## 220 0.6350 nan 0.0100 0.0002
## 240 0.6173 nan 0.0100 0.0000
## 260 0.5993 nan 0.0100 0.0002
## 280 0.5819 nan 0.0100 0.0000
## 300 0.5666 nan 0.0100 0.0002
## 320 0.5534 nan 0.0100 0.0000
## 340 0.5402 nan 0.0100 -0.0000
## 360 0.5269 nan 0.0100 -0.0001
## 380 0.5148 nan 0.0100 -0.0001
## 400 0.5034 nan 0.0100 0.0000
## 420 0.4923 nan 0.0100 -0.0000
## 440 0.4817 nan 0.0100 0.0002
## 460 0.4705 nan 0.0100 -0.0002
## 480 0.4614 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0044
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2938 nan 0.0100 0.0041
## 4 1.2850 nan 0.0100 0.0037
## 5 1.2767 nan 0.0100 0.0041
## 6 1.2681 nan 0.0100 0.0035
## 7 1.2594 nan 0.0100 0.0036
## 8 1.2513 nan 0.0100 0.0036
## 9 1.2432 nan 0.0100 0.0039
## 10 1.2352 nan 0.0100 0.0036
## 20 1.1644 nan 0.0100 0.0029
## 40 1.0489 nan 0.0100 0.0018
## 60 0.9600 nan 0.0100 0.0016
## 80 0.8907 nan 0.0100 0.0012
## 100 0.8356 nan 0.0100 0.0006
## 120 0.7890 nan 0.0100 0.0008
## 140 0.7520 nan 0.0100 0.0005
## 160 0.7187 nan 0.0100 0.0006
## 180 0.6915 nan 0.0100 0.0002
## 200 0.6673 nan 0.0100 0.0001
## 220 0.6444 nan 0.0100 -0.0001
## 240 0.6258 nan 0.0100 -0.0001
## 260 0.6075 nan 0.0100 0.0002
## 280 0.5922 nan 0.0100 0.0000
## 300 0.5778 nan 0.0100 0.0002
## 320 0.5653 nan 0.0100 -0.0000
## 340 0.5515 nan 0.0100 0.0002
## 360 0.5393 nan 0.0100 -0.0001
## 380 0.5274 nan 0.0100 0.0001
## 400 0.5159 nan 0.0100 -0.0000
## 420 0.5053 nan 0.0100 -0.0001
## 440 0.4946 nan 0.0100 -0.0001
## 460 0.4842 nan 0.0100 -0.0000
## 480 0.4747 nan 0.0100 -0.0001
## 500 0.4651 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2337 nan 0.1000 0.0392
## 2 1.1690 nan 0.1000 0.0307
## 3 1.1077 nan 0.1000 0.0251
## 4 1.0593 nan 0.1000 0.0213
## 5 1.0165 nan 0.1000 0.0154
## 6 0.9818 nan 0.1000 0.0132
## 7 0.9456 nan 0.1000 0.0128
## 8 0.9192 nan 0.1000 0.0114
## 9 0.8905 nan 0.1000 0.0111
## 10 0.8703 nan 0.1000 0.0069
## 20 0.7197 nan 0.1000 0.0012
## 40 0.5841 nan 0.1000 0.0009
## 60 0.5108 nan 0.1000 -0.0005
## 80 0.4516 nan 0.1000 -0.0006
## 100 0.4028 nan 0.1000 -0.0003
## 120 0.3538 nan 0.1000 -0.0019
## 140 0.3151 nan 0.1000 -0.0013
## 160 0.2817 nan 0.1000 -0.0009
## 180 0.2559 nan 0.1000 -0.0003
## 200 0.2306 nan 0.1000 -0.0002
## 220 0.2097 nan 0.1000 0.0001
## 240 0.1916 nan 0.1000 -0.0006
## 260 0.1767 nan 0.1000 -0.0002
## 280 0.1600 nan 0.1000 -0.0002
## 300 0.1463 nan 0.1000 -0.0003
## 320 0.1340 nan 0.1000 -0.0002
## 340 0.1204 nan 0.1000 0.0000
## 360 0.1100 nan 0.1000 -0.0002
## 380 0.1006 nan 0.1000 -0.0001
## 400 0.0931 nan 0.1000 -0.0002
## 420 0.0867 nan 0.1000 -0.0003
## 440 0.0795 nan 0.1000 -0.0003
## 460 0.0732 nan 0.1000 -0.0003
## 480 0.0679 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0399
## 2 1.1701 nan 0.1000 0.0307
## 3 1.1100 nan 0.1000 0.0240
## 4 1.0603 nan 0.1000 0.0212
## 5 1.0161 nan 0.1000 0.0195
## 6 0.9818 nan 0.1000 0.0133
## 7 0.9526 nan 0.1000 0.0112
## 8 0.9219 nan 0.1000 0.0143
## 9 0.8986 nan 0.1000 0.0098
## 10 0.8720 nan 0.1000 0.0099
## 20 0.7206 nan 0.1000 -0.0001
## 40 0.5895 nan 0.1000 -0.0015
## 60 0.5182 nan 0.1000 -0.0005
## 80 0.4565 nan 0.1000 -0.0008
## 100 0.4004 nan 0.1000 -0.0009
## 120 0.3546 nan 0.1000 -0.0009
## 140 0.3199 nan 0.1000 -0.0010
## 160 0.2923 nan 0.1000 -0.0009
## 180 0.2661 nan 0.1000 -0.0004
## 200 0.2416 nan 0.1000 -0.0005
## 220 0.2242 nan 0.1000 -0.0001
## 240 0.2037 nan 0.1000 -0.0007
## 260 0.1860 nan 0.1000 -0.0003
## 280 0.1696 nan 0.1000 -0.0003
## 300 0.1553 nan 0.1000 -0.0006
## 320 0.1439 nan 0.1000 -0.0009
## 340 0.1333 nan 0.1000 -0.0004
## 360 0.1228 nan 0.1000 -0.0006
## 380 0.1131 nan 0.1000 -0.0004
## 400 0.1047 nan 0.1000 -0.0004
## 420 0.0977 nan 0.1000 -0.0003
## 440 0.0894 nan 0.1000 -0.0002
## 460 0.0831 nan 0.1000 -0.0003
## 480 0.0771 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2367 nan 0.1000 0.0380
## 2 1.1688 nan 0.1000 0.0303
## 3 1.1094 nan 0.1000 0.0282
## 4 1.0593 nan 0.1000 0.0214
## 5 1.0180 nan 0.1000 0.0177
## 6 0.9795 nan 0.1000 0.0157
## 7 0.9419 nan 0.1000 0.0144
## 8 0.9133 nan 0.1000 0.0119
## 9 0.8899 nan 0.1000 0.0085
## 10 0.8686 nan 0.1000 0.0077
## 20 0.7267 nan 0.1000 0.0023
## 40 0.5945 nan 0.1000 -0.0006
## 60 0.5221 nan 0.1000 -0.0002
## 80 0.4607 nan 0.1000 -0.0005
## 100 0.4196 nan 0.1000 -0.0007
## 120 0.3841 nan 0.1000 -0.0006
## 140 0.3422 nan 0.1000 -0.0000
## 160 0.3064 nan 0.1000 -0.0010
## 180 0.2829 nan 0.1000 -0.0010
## 200 0.2595 nan 0.1000 -0.0001
## 220 0.2365 nan 0.1000 -0.0005
## 240 0.2162 nan 0.1000 -0.0008
## 260 0.1997 nan 0.1000 -0.0001
## 280 0.1832 nan 0.1000 -0.0008
## 300 0.1677 nan 0.1000 -0.0007
## 320 0.1550 nan 0.1000 -0.0008
## 340 0.1406 nan 0.1000 -0.0002
## 360 0.1299 nan 0.1000 -0.0007
## 380 0.1199 nan 0.1000 -0.0004
## 400 0.1092 nan 0.1000 -0.0001
## 420 0.1011 nan 0.1000 -0.0006
## 440 0.0939 nan 0.1000 -0.0003
## 460 0.0864 nan 0.1000 -0.0002
## 480 0.0813 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0361
## 2 1.1587 nan 0.1000 0.0378
## 3 1.0954 nan 0.1000 0.0289
## 4 1.0418 nan 0.1000 0.0207
## 5 0.9956 nan 0.1000 0.0201
## 6 0.9566 nan 0.1000 0.0149
## 7 0.9245 nan 0.1000 0.0118
## 8 0.8899 nan 0.1000 0.0154
## 9 0.8620 nan 0.1000 0.0114
## 10 0.8359 nan 0.1000 0.0084
## 20 0.6763 nan 0.1000 0.0025
## 40 0.5433 nan 0.1000 0.0005
## 60 0.4520 nan 0.1000 -0.0005
## 80 0.3856 nan 0.1000 -0.0003
## 100 0.3382 nan 0.1000 -0.0005
## 120 0.2954 nan 0.1000 -0.0009
## 140 0.2597 nan 0.1000 -0.0003
## 160 0.2292 nan 0.1000 -0.0008
## 180 0.2055 nan 0.1000 0.0001
## 200 0.1797 nan 0.1000 0.0001
## 220 0.1583 nan 0.1000 -0.0003
## 240 0.1396 nan 0.1000 -0.0006
## 260 0.1234 nan 0.1000 -0.0002
## 280 0.1104 nan 0.1000 -0.0003
## 300 0.0993 nan 0.1000 -0.0002
## 320 0.0895 nan 0.1000 -0.0003
## 340 0.0807 nan 0.1000 -0.0001
## 360 0.0728 nan 0.1000 -0.0001
## 380 0.0661 nan 0.1000 -0.0002
## 400 0.0597 nan 0.1000 -0.0001
## 420 0.0540 nan 0.1000 -0.0003
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## 460 0.0447 nan 0.1000 -0.0000
## 480 0.0409 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0403
## 2 1.1645 nan 0.1000 0.0335
## 3 1.1017 nan 0.1000 0.0279
## 4 1.0505 nan 0.1000 0.0205
## 5 1.0067 nan 0.1000 0.0219
## 6 0.9663 nan 0.1000 0.0168
## 7 0.9313 nan 0.1000 0.0142
## 8 0.9046 nan 0.1000 0.0091
## 9 0.8769 nan 0.1000 0.0106
## 10 0.8496 nan 0.1000 0.0113
## 20 0.6862 nan 0.1000 0.0002
## 40 0.5537 nan 0.1000 -0.0035
## 60 0.4607 nan 0.1000 -0.0002
## 80 0.3971 nan 0.1000 -0.0018
## 100 0.3405 nan 0.1000 -0.0013
## 120 0.2992 nan 0.1000 -0.0011
## 140 0.2610 nan 0.1000 -0.0015
## 160 0.2296 nan 0.1000 -0.0008
## 180 0.2001 nan 0.1000 -0.0002
## 200 0.1786 nan 0.1000 -0.0006
## 220 0.1568 nan 0.1000 -0.0003
## 240 0.1377 nan 0.1000 -0.0003
## 260 0.1237 nan 0.1000 -0.0003
## 280 0.1097 nan 0.1000 -0.0003
## 300 0.0991 nan 0.1000 -0.0003
## 320 0.0905 nan 0.1000 -0.0001
## 340 0.0807 nan 0.1000 -0.0001
## 360 0.0732 nan 0.1000 -0.0003
## 380 0.0659 nan 0.1000 -0.0002
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## 460 0.0450 nan 0.1000 -0.0001
## 480 0.0409 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0391
## 2 1.1585 nan 0.1000 0.0318
## 3 1.0962 nan 0.1000 0.0278
## 4 1.0445 nan 0.1000 0.0229
## 5 0.9953 nan 0.1000 0.0214
## 6 0.9532 nan 0.1000 0.0164
## 7 0.9210 nan 0.1000 0.0136
## 8 0.8922 nan 0.1000 0.0122
## 9 0.8656 nan 0.1000 0.0096
## 10 0.8408 nan 0.1000 0.0088
## 20 0.6867 nan 0.1000 0.0020
## 40 0.5505 nan 0.1000 0.0005
## 60 0.4689 nan 0.1000 -0.0001
## 80 0.4071 nan 0.1000 -0.0012
## 100 0.3501 nan 0.1000 -0.0006
## 120 0.3076 nan 0.1000 -0.0014
## 140 0.2699 nan 0.1000 -0.0009
## 160 0.2396 nan 0.1000 -0.0009
## 180 0.2125 nan 0.1000 -0.0002
## 200 0.1897 nan 0.1000 -0.0006
## 220 0.1689 nan 0.1000 -0.0005
## 240 0.1506 nan 0.1000 -0.0009
## 260 0.1347 nan 0.1000 -0.0005
## 280 0.1204 nan 0.1000 -0.0002
## 300 0.1082 nan 0.1000 -0.0005
## 320 0.0982 nan 0.1000 -0.0006
## 340 0.0886 nan 0.1000 -0.0003
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## 380 0.0731 nan 0.1000 -0.0002
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## 480 0.0458 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2278 nan 0.1000 0.0408
## 2 1.1612 nan 0.1000 0.0271
## 3 1.0993 nan 0.1000 0.0240
## 4 1.0385 nan 0.1000 0.0243
## 5 0.9870 nan 0.1000 0.0236
## 6 0.9473 nan 0.1000 0.0167
## 7 0.9082 nan 0.1000 0.0162
## 8 0.8742 nan 0.1000 0.0126
## 9 0.8434 nan 0.1000 0.0110
## 10 0.8180 nan 0.1000 0.0093
## 20 0.6514 nan 0.1000 0.0033
## 40 0.4848 nan 0.1000 0.0008
## 60 0.3953 nan 0.1000 -0.0009
## 80 0.3238 nan 0.1000 -0.0011
## 100 0.2703 nan 0.1000 -0.0009
## 120 0.2294 nan 0.1000 -0.0005
## 140 0.1966 nan 0.1000 -0.0005
## 160 0.1667 nan 0.1000 -0.0005
## 180 0.1441 nan 0.1000 -0.0001
## 200 0.1249 nan 0.1000 -0.0006
## 220 0.1108 nan 0.1000 -0.0004
## 240 0.0950 nan 0.1000 -0.0003
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## 280 0.0730 nan 0.1000 -0.0000
## 300 0.0657 nan 0.1000 -0.0001
## 320 0.0586 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0369
## 2 1.1489 nan 0.1000 0.0330
## 3 1.0807 nan 0.1000 0.0310
## 4 1.0243 nan 0.1000 0.0238
## 5 0.9795 nan 0.1000 0.0180
## 6 0.9342 nan 0.1000 0.0173
## 7 0.8992 nan 0.1000 0.0145
## 8 0.8627 nan 0.1000 0.0149
## 9 0.8343 nan 0.1000 0.0118
## 10 0.8089 nan 0.1000 0.0076
## 20 0.6499 nan 0.1000 0.0012
## 40 0.5033 nan 0.1000 -0.0004
## 60 0.4034 nan 0.1000 -0.0024
## 80 0.3358 nan 0.1000 0.0006
## 100 0.2801 nan 0.1000 -0.0015
## 120 0.2370 nan 0.1000 -0.0011
## 140 0.2026 nan 0.1000 -0.0010
## 160 0.1730 nan 0.1000 -0.0002
## 180 0.1488 nan 0.1000 -0.0008
## 200 0.1306 nan 0.1000 -0.0003
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## 240 0.1017 nan 0.1000 0.0000
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## 280 0.0773 nan 0.1000 -0.0002
## 300 0.0679 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2307 nan 0.1000 0.0413
## 2 1.1592 nan 0.1000 0.0298
## 3 1.1007 nan 0.1000 0.0263
## 4 1.0469 nan 0.1000 0.0231
## 5 0.9975 nan 0.1000 0.0208
## 6 0.9555 nan 0.1000 0.0167
## 7 0.9192 nan 0.1000 0.0128
## 8 0.8909 nan 0.1000 0.0096
## 9 0.8631 nan 0.1000 0.0102
## 10 0.8363 nan 0.1000 0.0095
## 20 0.6783 nan 0.1000 0.0007
## 40 0.5227 nan 0.1000 0.0004
## 60 0.4303 nan 0.1000 -0.0012
## 80 0.3577 nan 0.1000 -0.0006
## 100 0.3042 nan 0.1000 -0.0005
## 120 0.2632 nan 0.1000 -0.0007
## 140 0.2217 nan 0.1000 -0.0010
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## 180 0.1672 nan 0.1000 -0.0005
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## 480 0.0285 nan 0.1000 -0.0002
## 500 0.0253 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2721 nan 0.0010 0.0004
## 80 1.2571 nan 0.0010 0.0003
## 100 1.2425 nan 0.0010 0.0004
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2147 nan 0.0010 0.0003
## 160 1.2013 nan 0.0010 0.0003
## 180 1.1886 nan 0.0010 0.0003
## 200 1.1762 nan 0.0010 0.0003
## 220 1.1639 nan 0.0010 0.0003
## 240 1.1521 nan 0.0010 0.0002
## 260 1.1407 nan 0.0010 0.0003
## 280 1.1297 nan 0.0010 0.0002
## 300 1.1189 nan 0.0010 0.0002
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0986 nan 0.0010 0.0002
## 360 1.0889 nan 0.0010 0.0002
## 380 1.0794 nan 0.0010 0.0002
## 400 1.0700 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0521 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0348 nan 0.0010 0.0002
## 500 1.0266 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0003
## 60 1.2722 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2155 nan 0.0010 0.0003
## 160 1.2025 nan 0.0010 0.0003
## 180 1.1896 nan 0.0010 0.0003
## 200 1.1772 nan 0.0010 0.0003
## 220 1.1652 nan 0.0010 0.0002
## 240 1.1535 nan 0.0010 0.0002
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## 280 1.1313 nan 0.0010 0.0002
## 300 1.1207 nan 0.0010 0.0002
## 320 1.1103 nan 0.0010 0.0002
## 340 1.1003 nan 0.0010 0.0002
## 360 1.0903 nan 0.0010 0.0002
## 380 1.0809 nan 0.0010 0.0002
## 400 1.0715 nan 0.0010 0.0002
## 420 1.0625 nan 0.0010 0.0002
## 440 1.0537 nan 0.0010 0.0002
## 460 1.0448 nan 0.0010 0.0002
## 480 1.0366 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2727 nan 0.0010 0.0003
## 80 1.2577 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2154 nan 0.0010 0.0003
## 160 1.2022 nan 0.0010 0.0003
## 180 1.1894 nan 0.0010 0.0003
## 200 1.1771 nan 0.0010 0.0003
## 220 1.1652 nan 0.0010 0.0003
## 240 1.1537 nan 0.0010 0.0003
## 260 1.1425 nan 0.0010 0.0002
## 280 1.1316 nan 0.0010 0.0002
## 300 1.1210 nan 0.0010 0.0003
## 320 1.1107 nan 0.0010 0.0002
## 340 1.1009 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0723 nan 0.0010 0.0002
## 420 1.0631 nan 0.0010 0.0002
## 440 1.0542 nan 0.0010 0.0002
## 460 1.0456 nan 0.0010 0.0002
## 480 1.0373 nan 0.0010 0.0002
## 500 1.0291 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0003
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0003
## 100 1.2377 nan 0.0010 0.0003
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2083 nan 0.0010 0.0003
## 160 1.1941 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1668 nan 0.0010 0.0003
## 220 1.1540 nan 0.0010 0.0003
## 240 1.1417 nan 0.0010 0.0002
## 260 1.1296 nan 0.0010 0.0002
## 280 1.1179 nan 0.0010 0.0003
## 300 1.1064 nan 0.0010 0.0003
## 320 1.0953 nan 0.0010 0.0003
## 340 1.0844 nan 0.0010 0.0002
## 360 1.0743 nan 0.0010 0.0002
## 380 1.0641 nan 0.0010 0.0002
## 400 1.0541 nan 0.0010 0.0002
## 420 1.0445 nan 0.0010 0.0002
## 440 1.0350 nan 0.0010 0.0002
## 460 1.0261 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0084 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2534 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0004
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0004
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1808 nan 0.0010 0.0003
## 200 1.1676 nan 0.0010 0.0003
## 220 1.1547 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0002
## 260 1.1304 nan 0.0010 0.0003
## 280 1.1184 nan 0.0010 0.0002
## 300 1.1072 nan 0.0010 0.0002
## 320 1.0961 nan 0.0010 0.0003
## 340 1.0852 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0650 nan 0.0010 0.0002
## 400 1.0551 nan 0.0010 0.0002
## 420 1.0455 nan 0.0010 0.0002
## 440 1.0361 nan 0.0010 0.0002
## 460 1.0268 nan 0.0010 0.0002
## 480 1.0180 nan 0.0010 0.0002
## 500 1.0094 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0003
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0003
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0003
## 120 1.2226 nan 0.0010 0.0003
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
## 200 1.1673 nan 0.0010 0.0003
## 220 1.1546 nan 0.0010 0.0003
## 240 1.1422 nan 0.0010 0.0003
## 260 1.1300 nan 0.0010 0.0003
## 280 1.1184 nan 0.0010 0.0002
## 300 1.1071 nan 0.0010 0.0003
## 320 1.0960 nan 0.0010 0.0002
## 340 1.0856 nan 0.0010 0.0002
## 360 1.0753 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0557 nan 0.0010 0.0002
## 420 1.0463 nan 0.0010 0.0002
## 440 1.0370 nan 0.0010 0.0002
## 460 1.0277 nan 0.0010 0.0002
## 480 1.0189 nan 0.0010 0.0002
## 500 1.0103 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0005
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2659 nan 0.0010 0.0004
## 80 1.2488 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2163 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
## 160 1.1860 nan 0.0010 0.0003
## 180 1.1722 nan 0.0010 0.0003
## 200 1.1585 nan 0.0010 0.0003
## 220 1.1451 nan 0.0010 0.0003
## 240 1.1320 nan 0.0010 0.0003
## 260 1.1193 nan 0.0010 0.0003
## 280 1.1072 nan 0.0010 0.0003
## 300 1.0952 nan 0.0010 0.0002
## 320 1.0836 nan 0.0010 0.0003
## 340 1.0726 nan 0.0010 0.0003
## 360 1.0616 nan 0.0010 0.0002
## 380 1.0509 nan 0.0010 0.0002
## 400 1.0405 nan 0.0010 0.0002
## 420 1.0302 nan 0.0010 0.0002
## 440 1.0203 nan 0.0010 0.0002
## 460 1.0106 nan 0.0010 0.0002
## 480 1.0013 nan 0.0010 0.0002
## 500 0.9924 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3113 nan 0.0010 0.0005
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2499 nan 0.0010 0.0004
## 100 1.2338 nan 0.0010 0.0004
## 120 1.2182 nan 0.0010 0.0004
## 140 1.2029 nan 0.0010 0.0003
## 160 1.1883 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0003
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## 220 1.1463 nan 0.0010 0.0003
## 240 1.1334 nan 0.0010 0.0003
## 260 1.1209 nan 0.0010 0.0003
## 280 1.1083 nan 0.0010 0.0003
## 300 1.0966 nan 0.0010 0.0003
## 320 1.0850 nan 0.0010 0.0002
## 340 1.0738 nan 0.0010 0.0002
## 360 1.0631 nan 0.0010 0.0002
## 380 1.0525 nan 0.0010 0.0002
## 400 1.0420 nan 0.0010 0.0002
## 420 1.0320 nan 0.0010 0.0002
## 440 1.0225 nan 0.0010 0.0002
## 460 1.0129 nan 0.0010 0.0002
## 480 1.0035 nan 0.0010 0.0002
## 500 0.9946 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0005
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0003
## 80 1.2505 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2196 nan 0.0010 0.0004
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1896 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1617 nan 0.0010 0.0003
## 220 1.1485 nan 0.0010 0.0003
## 240 1.1356 nan 0.0010 0.0003
## 260 1.1233 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0996 nan 0.0010 0.0002
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0771 nan 0.0010 0.0002
## 360 1.0664 nan 0.0010 0.0002
## 380 1.0560 nan 0.0010 0.0002
## 400 1.0459 nan 0.0010 0.0002
## 420 1.0362 nan 0.0010 0.0002
## 440 1.0265 nan 0.0010 0.0002
## 460 1.0173 nan 0.0010 0.0002
## 480 1.0080 nan 0.0010 0.0002
## 500 0.9989 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2957 nan 0.0100 0.0036
## 4 1.2873 nan 0.0100 0.0036
## 5 1.2797 nan 0.0100 0.0034
## 6 1.2711 nan 0.0100 0.0036
## 7 1.2642 nan 0.0100 0.0031
## 8 1.2569 nan 0.0100 0.0032
## 9 1.2500 nan 0.0100 0.0030
## 10 1.2432 nan 0.0100 0.0032
## 20 1.1746 nan 0.0100 0.0029
## 40 1.0700 nan 0.0100 0.0018
## 60 0.9879 nan 0.0100 0.0014
## 80 0.9245 nan 0.0100 0.0011
## 100 0.8739 nan 0.0100 0.0009
## 120 0.8317 nan 0.0100 0.0004
## 140 0.7969 nan 0.0100 0.0004
## 160 0.7670 nan 0.0100 0.0003
## 180 0.7420 nan 0.0100 0.0001
## 200 0.7194 nan 0.0100 0.0002
## 220 0.7011 nan 0.0100 0.0002
## 240 0.6837 nan 0.0100 0.0000
## 260 0.6692 nan 0.0100 0.0000
## 280 0.6549 nan 0.0100 0.0001
## 300 0.6419 nan 0.0100 -0.0000
## 320 0.6292 nan 0.0100 0.0002
## 340 0.6174 nan 0.0100 0.0001
## 360 0.6058 nan 0.0100 0.0000
## 380 0.5948 nan 0.0100 0.0001
## 400 0.5846 nan 0.0100 0.0000
## 420 0.5760 nan 0.0100 -0.0001
## 440 0.5666 nan 0.0100 -0.0000
## 460 0.5581 nan 0.0100 -0.0001
## 480 0.5488 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0036
## 2 1.3041 nan 0.0100 0.0035
## 3 1.2960 nan 0.0100 0.0039
## 4 1.2882 nan 0.0100 0.0034
## 5 1.2802 nan 0.0100 0.0034
## 6 1.2725 nan 0.0100 0.0038
## 7 1.2651 nan 0.0100 0.0033
## 8 1.2573 nan 0.0100 0.0033
## 9 1.2496 nan 0.0100 0.0036
## 10 1.2429 nan 0.0100 0.0028
## 20 1.1758 nan 0.0100 0.0031
## 40 1.0696 nan 0.0100 0.0018
## 60 0.9907 nan 0.0100 0.0013
## 80 0.9244 nan 0.0100 0.0012
## 100 0.8730 nan 0.0100 0.0007
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## 140 0.7953 nan 0.0100 0.0007
## 160 0.7667 nan 0.0100 0.0005
## 180 0.7422 nan 0.0100 0.0003
## 200 0.7201 nan 0.0100 0.0004
## 220 0.7015 nan 0.0100 0.0003
## 240 0.6855 nan 0.0100 0.0000
## 260 0.6704 nan 0.0100 -0.0001
## 280 0.6568 nan 0.0100 -0.0000
## 300 0.6437 nan 0.0100 0.0001
## 320 0.6320 nan 0.0100 0.0001
## 340 0.6216 nan 0.0100 0.0001
## 360 0.6109 nan 0.0100 -0.0000
## 380 0.6017 nan 0.0100 0.0001
## 400 0.5923 nan 0.0100 -0.0001
## 420 0.5839 nan 0.0100 -0.0001
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## 460 0.5672 nan 0.0100 0.0000
## 480 0.5581 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3039 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0036
## 4 1.2884 nan 0.0100 0.0036
## 5 1.2809 nan 0.0100 0.0035
## 6 1.2732 nan 0.0100 0.0031
## 7 1.2663 nan 0.0100 0.0032
## 8 1.2595 nan 0.0100 0.0031
## 9 1.2528 nan 0.0100 0.0033
## 10 1.2455 nan 0.0100 0.0037
## 20 1.1790 nan 0.0100 0.0029
## 40 1.0755 nan 0.0100 0.0017
## 60 0.9948 nan 0.0100 0.0011
## 80 0.9301 nan 0.0100 0.0011
## 100 0.8780 nan 0.0100 0.0009
## 120 0.8360 nan 0.0100 0.0005
## 140 0.8005 nan 0.0100 0.0005
## 160 0.7721 nan 0.0100 0.0004
## 180 0.7466 nan 0.0100 0.0003
## 200 0.7267 nan 0.0100 0.0002
## 220 0.7078 nan 0.0100 -0.0001
## 240 0.6912 nan 0.0100 0.0002
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## 280 0.6632 nan 0.0100 0.0000
## 300 0.6504 nan 0.0100 -0.0000
## 320 0.6392 nan 0.0100 0.0001
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## 360 0.6174 nan 0.0100 -0.0001
## 380 0.6077 nan 0.0100 0.0001
## 400 0.5988 nan 0.0100 -0.0000
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## 460 0.5738 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2939 nan 0.0100 0.0039
## 4 1.2861 nan 0.0100 0.0035
## 5 1.2772 nan 0.0100 0.0037
## 6 1.2689 nan 0.0100 0.0034
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2522 nan 0.0100 0.0035
## 9 1.2448 nan 0.0100 0.0033
## 10 1.2364 nan 0.0100 0.0038
## 20 1.1657 nan 0.0100 0.0032
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9672 nan 0.0100 0.0016
## 80 0.8995 nan 0.0100 0.0011
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## 120 0.8015 nan 0.0100 0.0007
## 140 0.7648 nan 0.0100 0.0004
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## 320 0.5822 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3030 nan 0.0100 0.0039
## 3 1.2941 nan 0.0100 0.0039
## 4 1.2850 nan 0.0100 0.0039
## 5 1.2769 nan 0.0100 0.0033
## 6 1.2681 nan 0.0100 0.0040
## 7 1.2596 nan 0.0100 0.0035
## 8 1.2510 nan 0.0100 0.0037
## 9 1.2434 nan 0.0100 0.0037
## 10 1.2348 nan 0.0100 0.0037
## 20 1.1642 nan 0.0100 0.0033
## 40 1.0529 nan 0.0100 0.0020
## 60 0.9687 nan 0.0100 0.0017
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## 120 0.8067 nan 0.0100 0.0006
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## 320 0.5918 nan 0.0100 -0.0001
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## 380 0.5557 nan 0.0100 -0.0000
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## 460 0.5133 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0038
## 2 1.3031 nan 0.0100 0.0037
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2770 nan 0.0100 0.0041
## 6 1.2691 nan 0.0100 0.0038
## 7 1.2604 nan 0.0100 0.0040
## 8 1.2526 nan 0.0100 0.0032
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## 10 1.2363 nan 0.0100 0.0035
## 20 1.1679 nan 0.0100 0.0025
## 40 1.0532 nan 0.0100 0.0020
## 60 0.9679 nan 0.0100 0.0015
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## 500 0.5088 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0045
## 2 1.3012 nan 0.0100 0.0046
## 3 1.2918 nan 0.0100 0.0045
## 4 1.2824 nan 0.0100 0.0044
## 5 1.2737 nan 0.0100 0.0040
## 6 1.2649 nan 0.0100 0.0040
## 7 1.2562 nan 0.0100 0.0038
## 8 1.2483 nan 0.0100 0.0039
## 9 1.2402 nan 0.0100 0.0031
## 10 1.2319 nan 0.0100 0.0038
## 20 1.1586 nan 0.0100 0.0027
## 40 1.0401 nan 0.0100 0.0022
## 60 0.9509 nan 0.0100 0.0014
## 80 0.8819 nan 0.0100 0.0013
## 100 0.8257 nan 0.0100 0.0010
## 120 0.7800 nan 0.0100 0.0009
## 140 0.7403 nan 0.0100 0.0003
## 160 0.7092 nan 0.0100 0.0003
## 180 0.6819 nan 0.0100 0.0003
## 200 0.6576 nan 0.0100 0.0001
## 220 0.6357 nan 0.0100 0.0003
## 240 0.6145 nan 0.0100 0.0003
## 260 0.5962 nan 0.0100 0.0001
## 280 0.5784 nan 0.0100 0.0002
## 300 0.5631 nan 0.0100 0.0000
## 320 0.5472 nan 0.0100 0.0002
## 340 0.5336 nan 0.0100 -0.0001
## 360 0.5191 nan 0.0100 0.0001
## 380 0.5068 nan 0.0100 -0.0001
## 400 0.4951 nan 0.0100 0.0001
## 420 0.4832 nan 0.0100 -0.0000
## 440 0.4723 nan 0.0100 0.0000
## 460 0.4624 nan 0.0100 -0.0000
## 480 0.4522 nan 0.0100 -0.0000
## 500 0.4426 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3023 nan 0.0100 0.0044
## 3 1.2933 nan 0.0100 0.0044
## 4 1.2839 nan 0.0100 0.0042
## 5 1.2739 nan 0.0100 0.0041
## 6 1.2658 nan 0.0100 0.0038
## 7 1.2578 nan 0.0100 0.0036
## 8 1.2503 nan 0.0100 0.0036
## 9 1.2418 nan 0.0100 0.0036
## 10 1.2327 nan 0.0100 0.0037
## 20 1.1570 nan 0.0100 0.0032
## 40 1.0400 nan 0.0100 0.0018
## 60 0.9505 nan 0.0100 0.0015
## 80 0.8804 nan 0.0100 0.0014
## 100 0.8243 nan 0.0100 0.0007
## 120 0.7783 nan 0.0100 0.0008
## 140 0.7421 nan 0.0100 0.0004
## 160 0.7109 nan 0.0100 0.0003
## 180 0.6831 nan 0.0100 0.0002
## 200 0.6578 nan 0.0100 0.0002
## 220 0.6376 nan 0.0100 -0.0000
## 240 0.6165 nan 0.0100 0.0001
## 260 0.5986 nan 0.0100 0.0003
## 280 0.5821 nan 0.0100 0.0003
## 300 0.5668 nan 0.0100 -0.0002
## 320 0.5529 nan 0.0100 0.0000
## 340 0.5393 nan 0.0100 -0.0001
## 360 0.5268 nan 0.0100 -0.0001
## 380 0.5143 nan 0.0100 0.0000
## 400 0.5030 nan 0.0100 -0.0003
## 420 0.4907 nan 0.0100 0.0001
## 440 0.4797 nan 0.0100 0.0001
## 460 0.4695 nan 0.0100 0.0000
## 480 0.4603 nan 0.0100 -0.0000
## 500 0.4516 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0045
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2935 nan 0.0100 0.0040
## 4 1.2853 nan 0.0100 0.0038
## 5 1.2764 nan 0.0100 0.0043
## 6 1.2673 nan 0.0100 0.0041
## 7 1.2592 nan 0.0100 0.0036
## 8 1.2503 nan 0.0100 0.0041
## 9 1.2424 nan 0.0100 0.0038
## 10 1.2337 nan 0.0100 0.0035
## 20 1.1603 nan 0.0100 0.0028
## 40 1.0434 nan 0.0100 0.0023
## 60 0.9569 nan 0.0100 0.0017
## 80 0.8892 nan 0.0100 0.0010
## 100 0.8339 nan 0.0100 0.0010
## 120 0.7884 nan 0.0100 0.0006
## 140 0.7507 nan 0.0100 0.0004
## 160 0.7187 nan 0.0100 0.0003
## 180 0.6906 nan 0.0100 0.0001
## 200 0.6669 nan 0.0100 0.0003
## 220 0.6468 nan 0.0100 0.0002
## 240 0.6268 nan 0.0100 0.0001
## 260 0.6091 nan 0.0100 0.0001
## 280 0.5935 nan 0.0100 0.0001
## 300 0.5786 nan 0.0100 -0.0001
## 320 0.5645 nan 0.0100 -0.0000
## 340 0.5512 nan 0.0100 0.0001
## 360 0.5393 nan 0.0100 -0.0001
## 380 0.5277 nan 0.0100 0.0000
## 400 0.5175 nan 0.0100 -0.0001
## 420 0.5071 nan 0.0100 -0.0003
## 440 0.4969 nan 0.0100 0.0002
## 460 0.4866 nan 0.0100 -0.0002
## 480 0.4765 nan 0.0100 -0.0000
## 500 0.4663 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0360
## 2 1.1745 nan 0.1000 0.0309
## 3 1.1186 nan 0.1000 0.0236
## 4 1.0661 nan 0.1000 0.0221
## 5 1.0243 nan 0.1000 0.0174
## 6 0.9889 nan 0.1000 0.0128
## 7 0.9577 nan 0.1000 0.0127
## 8 0.9232 nan 0.1000 0.0139
## 9 0.8942 nan 0.1000 0.0115
## 10 0.8699 nan 0.1000 0.0090
## 20 0.7230 nan 0.1000 0.0006
## 40 0.5935 nan 0.1000 0.0016
## 60 0.5053 nan 0.1000 0.0014
## 80 0.4435 nan 0.1000 0.0003
## 100 0.3895 nan 0.1000 -0.0009
## 120 0.3434 nan 0.1000 -0.0005
## 140 0.3083 nan 0.1000 -0.0003
## 160 0.2794 nan 0.1000 -0.0004
## 180 0.2531 nan 0.1000 -0.0011
## 200 0.2295 nan 0.1000 -0.0005
## 220 0.2042 nan 0.1000 -0.0012
## 240 0.1846 nan 0.1000 0.0003
## 260 0.1665 nan 0.1000 -0.0002
## 280 0.1526 nan 0.1000 -0.0001
## 300 0.1383 nan 0.1000 -0.0001
## 320 0.1279 nan 0.1000 -0.0002
## 340 0.1177 nan 0.1000 -0.0006
## 360 0.1093 nan 0.1000 -0.0003
## 380 0.0995 nan 0.1000 -0.0005
## 400 0.0913 nan 0.1000 -0.0003
## 420 0.0843 nan 0.1000 -0.0001
## 440 0.0790 nan 0.1000 -0.0001
## 460 0.0726 nan 0.1000 0.0000
## 480 0.0667 nan 0.1000 -0.0001
## 500 0.0614 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0393
## 2 1.1630 nan 0.1000 0.0292
## 3 1.1049 nan 0.1000 0.0266
## 4 1.0584 nan 0.1000 0.0212
## 5 1.0144 nan 0.1000 0.0192
## 6 0.9793 nan 0.1000 0.0152
## 7 0.9469 nan 0.1000 0.0131
## 8 0.9194 nan 0.1000 0.0112
## 9 0.8943 nan 0.1000 0.0110
## 10 0.8719 nan 0.1000 0.0097
## 20 0.7220 nan 0.1000 0.0018
## 40 0.5906 nan 0.1000 -0.0009
## 60 0.5067 nan 0.1000 -0.0007
## 80 0.4486 nan 0.1000 -0.0002
## 100 0.3989 nan 0.1000 -0.0011
## 120 0.3550 nan 0.1000 -0.0006
## 140 0.3216 nan 0.1000 -0.0005
## 160 0.2877 nan 0.1000 -0.0001
## 180 0.2637 nan 0.1000 -0.0009
## 200 0.2403 nan 0.1000 -0.0004
## 220 0.2203 nan 0.1000 -0.0003
## 240 0.1983 nan 0.1000 -0.0009
## 260 0.1816 nan 0.1000 -0.0004
## 280 0.1648 nan 0.1000 -0.0001
## 300 0.1503 nan 0.1000 -0.0006
## 320 0.1389 nan 0.1000 -0.0007
## 340 0.1274 nan 0.1000 -0.0002
## 360 0.1183 nan 0.1000 -0.0002
## 380 0.1096 nan 0.1000 -0.0003
## 400 0.1016 nan 0.1000 -0.0004
## 420 0.0944 nan 0.1000 -0.0007
## 440 0.0870 nan 0.1000 -0.0004
## 460 0.0807 nan 0.1000 0.0001
## 480 0.0747 nan 0.1000 -0.0001
## 500 0.0684 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0374
## 2 1.1726 nan 0.1000 0.0339
## 3 1.1180 nan 0.1000 0.0255
## 4 1.0736 nan 0.1000 0.0196
## 5 1.0306 nan 0.1000 0.0191
## 6 0.9940 nan 0.1000 0.0157
## 7 0.9576 nan 0.1000 0.0140
## 8 0.9298 nan 0.1000 0.0104
## 9 0.8999 nan 0.1000 0.0106
## 10 0.8768 nan 0.1000 0.0102
## 20 0.7321 nan 0.1000 0.0013
## 40 0.6123 nan 0.1000 0.0004
## 60 0.5358 nan 0.1000 -0.0033
## 80 0.4672 nan 0.1000 -0.0004
## 100 0.4119 nan 0.1000 -0.0008
## 120 0.3707 nan 0.1000 -0.0003
## 140 0.3345 nan 0.1000 -0.0006
## 160 0.3012 nan 0.1000 -0.0009
## 180 0.2732 nan 0.1000 -0.0007
## 200 0.2487 nan 0.1000 -0.0007
## 220 0.2282 nan 0.1000 -0.0006
## 240 0.2120 nan 0.1000 -0.0006
## 260 0.1950 nan 0.1000 -0.0008
## 280 0.1784 nan 0.1000 -0.0004
## 300 0.1651 nan 0.1000 -0.0003
## 320 0.1519 nan 0.1000 -0.0004
## 340 0.1397 nan 0.1000 -0.0004
## 360 0.1286 nan 0.1000 -0.0003
## 380 0.1187 nan 0.1000 -0.0004
## 400 0.1101 nan 0.1000 -0.0004
## 420 0.1011 nan 0.1000 -0.0006
## 440 0.0944 nan 0.1000 -0.0003
## 460 0.0873 nan 0.1000 -0.0004
## 480 0.0806 nan 0.1000 -0.0002
## 500 0.0756 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2291 nan 0.1000 0.0392
## 2 1.1582 nan 0.1000 0.0350
## 3 1.1002 nan 0.1000 0.0249
## 4 1.0481 nan 0.1000 0.0213
## 5 1.0032 nan 0.1000 0.0181
## 6 0.9614 nan 0.1000 0.0179
## 7 0.9210 nan 0.1000 0.0164
## 8 0.8883 nan 0.1000 0.0138
## 9 0.8563 nan 0.1000 0.0139
## 10 0.8318 nan 0.1000 0.0117
## 20 0.6736 nan 0.1000 0.0027
## 40 0.5299 nan 0.1000 -0.0014
## 60 0.4375 nan 0.1000 -0.0014
## 80 0.3740 nan 0.1000 0.0004
## 100 0.3244 nan 0.1000 -0.0021
## 120 0.2793 nan 0.1000 -0.0007
## 140 0.2432 nan 0.1000 -0.0000
## 160 0.2140 nan 0.1000 -0.0001
## 180 0.1906 nan 0.1000 -0.0005
## 200 0.1679 nan 0.1000 -0.0003
## 220 0.1488 nan 0.1000 -0.0001
## 240 0.1327 nan 0.1000 -0.0000
## 260 0.1172 nan 0.1000 -0.0003
## 280 0.1050 nan 0.1000 0.0000
## 300 0.0957 nan 0.1000 -0.0001
## 320 0.0866 nan 0.1000 -0.0003
## 340 0.0780 nan 0.1000 -0.0001
## 360 0.0704 nan 0.1000 -0.0001
## 380 0.0635 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 -0.0001
## 420 0.0523 nan 0.1000 -0.0001
## 440 0.0473 nan 0.1000 -0.0001
## 460 0.0430 nan 0.1000 -0.0002
## 480 0.0387 nan 0.1000 -0.0002
## 500 0.0350 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2350 nan 0.1000 0.0414
## 2 1.1585 nan 0.1000 0.0321
## 3 1.0995 nan 0.1000 0.0281
## 4 1.0494 nan 0.1000 0.0204
## 5 1.0084 nan 0.1000 0.0175
## 6 0.9715 nan 0.1000 0.0183
## 7 0.9372 nan 0.1000 0.0138
## 8 0.9048 nan 0.1000 0.0127
## 9 0.8754 nan 0.1000 0.0113
## 10 0.8474 nan 0.1000 0.0124
## 20 0.6965 nan 0.1000 0.0024
## 40 0.5512 nan 0.1000 0.0001
## 60 0.4637 nan 0.1000 -0.0009
## 80 0.3966 nan 0.1000 -0.0009
## 100 0.3408 nan 0.1000 -0.0007
## 120 0.2996 nan 0.1000 -0.0012
## 140 0.2620 nan 0.1000 -0.0004
## 160 0.2272 nan 0.1000 -0.0002
## 180 0.2000 nan 0.1000 -0.0013
## 200 0.1790 nan 0.1000 -0.0007
## 220 0.1602 nan 0.1000 -0.0005
## 240 0.1441 nan 0.1000 -0.0005
## 260 0.1278 nan 0.1000 -0.0004
## 280 0.1158 nan 0.1000 -0.0005
## 300 0.1037 nan 0.1000 -0.0002
## 320 0.0939 nan 0.1000 -0.0003
## 340 0.0846 nan 0.1000 -0.0002
## 360 0.0760 nan 0.1000 -0.0004
## 380 0.0692 nan 0.1000 -0.0002
## 400 0.0618 nan 0.1000 -0.0000
## 420 0.0560 nan 0.1000 -0.0001
## 440 0.0509 nan 0.1000 -0.0002
## 460 0.0456 nan 0.1000 -0.0001
## 480 0.0412 nan 0.1000 -0.0002
## 500 0.0373 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2325 nan 0.1000 0.0383
## 2 1.1511 nan 0.1000 0.0335
## 3 1.0909 nan 0.1000 0.0261
## 4 1.0436 nan 0.1000 0.0236
## 5 1.0025 nan 0.1000 0.0175
## 6 0.9615 nan 0.1000 0.0174
## 7 0.9295 nan 0.1000 0.0132
## 8 0.8985 nan 0.1000 0.0121
## 9 0.8732 nan 0.1000 0.0098
## 10 0.8449 nan 0.1000 0.0107
## 20 0.6955 nan 0.1000 0.0022
## 40 0.5615 nan 0.1000 -0.0002
## 60 0.4828 nan 0.1000 -0.0014
## 80 0.4142 nan 0.1000 -0.0008
## 100 0.3555 nan 0.1000 0.0000
## 120 0.3105 nan 0.1000 -0.0010
## 140 0.2753 nan 0.1000 -0.0012
## 160 0.2436 nan 0.1000 -0.0009
## 180 0.2173 nan 0.1000 -0.0003
## 200 0.1953 nan 0.1000 -0.0000
## 220 0.1713 nan 0.1000 -0.0003
## 240 0.1529 nan 0.1000 -0.0004
## 260 0.1367 nan 0.1000 -0.0000
## 280 0.1222 nan 0.1000 -0.0003
## 300 0.1110 nan 0.1000 -0.0006
## 320 0.1010 nan 0.1000 -0.0007
## 340 0.0909 nan 0.1000 -0.0003
## 360 0.0833 nan 0.1000 -0.0001
## 380 0.0754 nan 0.1000 -0.0002
## 400 0.0682 nan 0.1000 -0.0003
## 420 0.0621 nan 0.1000 -0.0004
## 440 0.0561 nan 0.1000 -0.0003
## 460 0.0510 nan 0.1000 -0.0002
## 480 0.0468 nan 0.1000 -0.0002
## 500 0.0427 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.1000 0.0439
## 2 1.1514 nan 0.1000 0.0352
## 3 1.0846 nan 0.1000 0.0292
## 4 1.0275 nan 0.1000 0.0237
## 5 0.9821 nan 0.1000 0.0195
## 6 0.9369 nan 0.1000 0.0175
## 7 0.9041 nan 0.1000 0.0105
## 8 0.8735 nan 0.1000 0.0105
## 9 0.8428 nan 0.1000 0.0104
## 10 0.8193 nan 0.1000 0.0068
## 20 0.6568 nan 0.1000 0.0020
## 40 0.4914 nan 0.1000 0.0008
## 60 0.4047 nan 0.1000 -0.0019
## 80 0.3360 nan 0.1000 -0.0006
## 100 0.2801 nan 0.1000 -0.0009
## 120 0.2355 nan 0.1000 -0.0007
## 140 0.2027 nan 0.1000 -0.0005
## 160 0.1725 nan 0.1000 -0.0007
## 180 0.1501 nan 0.1000 -0.0001
## 200 0.1315 nan 0.1000 -0.0001
## 220 0.1154 nan 0.1000 -0.0000
## 240 0.0995 nan 0.1000 -0.0002
## 260 0.0851 nan 0.1000 0.0001
## 280 0.0751 nan 0.1000 -0.0003
## 300 0.0650 nan 0.1000 -0.0000
## 320 0.0576 nan 0.1000 -0.0002
## 340 0.0512 nan 0.1000 -0.0000
## 360 0.0449 nan 0.1000 -0.0000
## 380 0.0393 nan 0.1000 -0.0001
## 400 0.0348 nan 0.1000 -0.0000
## 420 0.0312 nan 0.1000 -0.0000
## 440 0.0277 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0001
## 500 0.0194 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0366
## 2 1.1574 nan 0.1000 0.0348
## 3 1.0967 nan 0.1000 0.0291
## 4 1.0447 nan 0.1000 0.0251
## 5 0.9985 nan 0.1000 0.0199
## 6 0.9577 nan 0.1000 0.0181
## 7 0.9219 nan 0.1000 0.0144
## 8 0.8901 nan 0.1000 0.0121
## 9 0.8653 nan 0.1000 0.0091
## 10 0.8386 nan 0.1000 0.0104
## 20 0.6714 nan 0.1000 0.0021
## 40 0.5116 nan 0.1000 -0.0019
## 60 0.4142 nan 0.1000 0.0001
## 80 0.3401 nan 0.1000 0.0010
## 100 0.2891 nan 0.1000 -0.0014
## 120 0.2475 nan 0.1000 -0.0005
## 140 0.2132 nan 0.1000 -0.0008
## 160 0.1815 nan 0.1000 -0.0008
## 180 0.1580 nan 0.1000 -0.0004
## 200 0.1355 nan 0.1000 -0.0003
## 220 0.1184 nan 0.1000 -0.0005
## 240 0.1038 nan 0.1000 -0.0004
## 260 0.0913 nan 0.1000 -0.0003
## 280 0.0802 nan 0.1000 -0.0004
## 300 0.0711 nan 0.1000 -0.0005
## 320 0.0626 nan 0.1000 -0.0001
## 340 0.0552 nan 0.1000 -0.0001
## 360 0.0494 nan 0.1000 -0.0002
## 380 0.0435 nan 0.1000 -0.0001
## 400 0.0385 nan 0.1000 -0.0001
## 420 0.0345 nan 0.1000 -0.0001
## 440 0.0307 nan 0.1000 -0.0000
## 460 0.0276 nan 0.1000 -0.0001
## 480 0.0244 nan 0.1000 -0.0001
## 500 0.0220 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2301 nan 0.1000 0.0381
## 2 1.1508 nan 0.1000 0.0316
## 3 1.0905 nan 0.1000 0.0249
## 4 1.0317 nan 0.1000 0.0238
## 5 0.9916 nan 0.1000 0.0170
## 6 0.9547 nan 0.1000 0.0154
## 7 0.9219 nan 0.1000 0.0145
## 8 0.8917 nan 0.1000 0.0125
## 9 0.8612 nan 0.1000 0.0099
## 10 0.8355 nan 0.1000 0.0095
## 20 0.6814 nan 0.1000 -0.0003
## 40 0.5344 nan 0.1000 0.0003
## 60 0.4413 nan 0.1000 -0.0021
## 80 0.3684 nan 0.1000 -0.0016
## 100 0.3131 nan 0.1000 -0.0007
## 120 0.2670 nan 0.1000 -0.0003
## 140 0.2287 nan 0.1000 -0.0014
## 160 0.1982 nan 0.1000 -0.0008
## 180 0.1719 nan 0.1000 -0.0007
## 200 0.1485 nan 0.1000 -0.0004
## 220 0.1296 nan 0.1000 -0.0010
## 240 0.1136 nan 0.1000 -0.0006
## 260 0.0999 nan 0.1000 -0.0006
## 280 0.0885 nan 0.1000 -0.0006
## 300 0.0781 nan 0.1000 -0.0002
## 320 0.0688 nan 0.1000 -0.0003
## 340 0.0613 nan 0.1000 -0.0003
## 360 0.0550 nan 0.1000 -0.0001
## 380 0.0489 nan 0.1000 -0.0002
## 400 0.0433 nan 0.1000 -0.0001
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## 460 0.0312 nan 0.1000 -0.0001
## 480 0.0281 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0004
## 100 1.2427 nan 0.0010 0.0003
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2150 nan 0.0010 0.0003
## 160 1.2017 nan 0.0010 0.0003
## 180 1.1888 nan 0.0010 0.0003
## 200 1.1765 nan 0.0010 0.0002
## 220 1.1647 nan 0.0010 0.0002
## 240 1.1529 nan 0.0010 0.0002
## 260 1.1414 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1197 nan 0.0010 0.0002
## 320 1.1093 nan 0.0010 0.0002
## 340 1.0992 nan 0.0010 0.0002
## 360 1.0892 nan 0.0010 0.0002
## 380 1.0795 nan 0.0010 0.0002
## 400 1.0700 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0521 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0349 nan 0.0010 0.0002
## 500 1.0268 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2726 nan 0.0010 0.0004
## 80 1.2576 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2152 nan 0.0010 0.0003
## 160 1.2022 nan 0.0010 0.0003
## 180 1.1895 nan 0.0010 0.0003
## 200 1.1770 nan 0.0010 0.0003
## 220 1.1650 nan 0.0010 0.0003
## 240 1.1532 nan 0.0010 0.0003
## 260 1.1419 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1200 nan 0.0010 0.0002
## 320 1.1097 nan 0.0010 0.0002
## 340 1.0994 nan 0.0010 0.0003
## 360 1.0894 nan 0.0010 0.0002
## 380 1.0799 nan 0.0010 0.0002
## 400 1.0705 nan 0.0010 0.0002
## 420 1.0611 nan 0.0010 0.0002
## 440 1.0522 nan 0.0010 0.0002
## 460 1.0438 nan 0.0010 0.0002
## 480 1.0356 nan 0.0010 0.0002
## 500 1.0273 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2579 nan 0.0010 0.0004
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2296 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2031 nan 0.0010 0.0003
## 180 1.1906 nan 0.0010 0.0003
## 200 1.1782 nan 0.0010 0.0003
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1548 nan 0.0010 0.0003
## 260 1.1436 nan 0.0010 0.0002
## 280 1.1327 nan 0.0010 0.0002
## 300 1.1219 nan 0.0010 0.0002
## 320 1.1114 nan 0.0010 0.0002
## 340 1.1012 nan 0.0010 0.0002
## 360 1.0914 nan 0.0010 0.0002
## 380 1.0818 nan 0.0010 0.0002
## 400 1.0724 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
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## 460 1.0458 nan 0.0010 0.0002
## 480 1.0375 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2526 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0003
## 120 1.2224 nan 0.0010 0.0003
## 140 1.2076 nan 0.0010 0.0003
## 160 1.1939 nan 0.0010 0.0003
## 180 1.1804 nan 0.0010 0.0003
## 200 1.1673 nan 0.0010 0.0003
## 220 1.1543 nan 0.0010 0.0003
## 240 1.1420 nan 0.0010 0.0003
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## 280 1.1188 nan 0.0010 0.0002
## 300 1.1071 nan 0.0010 0.0003
## 320 1.0961 nan 0.0010 0.0002
## 340 1.0855 nan 0.0010 0.0002
## 360 1.0748 nan 0.0010 0.0002
## 380 1.0644 nan 0.0010 0.0002
## 400 1.0544 nan 0.0010 0.0002
## 420 1.0447 nan 0.0010 0.0002
## 440 1.0352 nan 0.0010 0.0002
## 460 1.0263 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0003
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0003
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1943 nan 0.0010 0.0003
## 180 1.1807 nan 0.0010 0.0002
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0003
## 240 1.1419 nan 0.0010 0.0002
## 260 1.1295 nan 0.0010 0.0003
## 280 1.1179 nan 0.0010 0.0003
## 300 1.1066 nan 0.0010 0.0002
## 320 1.0956 nan 0.0010 0.0002
## 340 1.0852 nan 0.0010 0.0002
## 360 1.0749 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0548 nan 0.0010 0.0002
## 420 1.0450 nan 0.0010 0.0002
## 440 1.0356 nan 0.0010 0.0002
## 460 1.0265 nan 0.0010 0.0002
## 480 1.0176 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0003
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0003
## 100 1.2390 nan 0.0010 0.0003
## 120 1.2246 nan 0.0010 0.0004
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0002
## 200 1.1697 nan 0.0010 0.0003
## 220 1.1570 nan 0.0010 0.0003
## 240 1.1448 nan 0.0010 0.0003
## 260 1.1331 nan 0.0010 0.0003
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1102 nan 0.0010 0.0002
## 320 1.0993 nan 0.0010 0.0002
## 340 1.0887 nan 0.0010 0.0002
## 360 1.0783 nan 0.0010 0.0002
## 380 1.0682 nan 0.0010 0.0002
## 400 1.0585 nan 0.0010 0.0002
## 420 1.0489 nan 0.0010 0.0002
## 440 1.0393 nan 0.0010 0.0002
## 460 1.0303 nan 0.0010 0.0002
## 480 1.0218 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0003
## 100 1.2332 nan 0.0010 0.0004
## 120 1.2173 nan 0.0010 0.0003
## 140 1.2022 nan 0.0010 0.0003
## 160 1.1875 nan 0.0010 0.0003
## 180 1.1729 nan 0.0010 0.0004
## 200 1.1591 nan 0.0010 0.0003
## 220 1.1457 nan 0.0010 0.0003
## 240 1.1324 nan 0.0010 0.0003
## 260 1.1200 nan 0.0010 0.0003
## 280 1.1079 nan 0.0010 0.0003
## 300 1.0960 nan 0.0010 0.0002
## 320 1.0845 nan 0.0010 0.0003
## 340 1.0731 nan 0.0010 0.0002
## 360 1.0625 nan 0.0010 0.0002
## 380 1.0519 nan 0.0010 0.0002
## 400 1.0414 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0217 nan 0.0010 0.0002
## 460 1.0122 nan 0.0010 0.0002
## 480 1.0027 nan 0.0010 0.0002
## 500 0.9938 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2668 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2032 nan 0.0010 0.0003
## 160 1.1887 nan 0.0010 0.0003
## 180 1.1745 nan 0.0010 0.0003
## 200 1.1605 nan 0.0010 0.0003
## 220 1.1474 nan 0.0010 0.0003
## 240 1.1345 nan 0.0010 0.0003
## 260 1.1221 nan 0.0010 0.0003
## 280 1.1102 nan 0.0010 0.0002
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0870 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0543 nan 0.0010 0.0002
## 400 1.0440 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0242 nan 0.0010 0.0002
## 460 1.0149 nan 0.0010 0.0002
## 480 1.0056 nan 0.0010 0.0002
## 500 0.9967 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2361 nan 0.0010 0.0003
## 120 1.2208 nan 0.0010 0.0003
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1914 nan 0.0010 0.0003
## 180 1.1776 nan 0.0010 0.0003
## 200 1.1640 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1380 nan 0.0010 0.0003
## 260 1.1257 nan 0.0010 0.0002
## 280 1.1138 nan 0.0010 0.0003
## 300 1.1023 nan 0.0010 0.0002
## 320 1.0910 nan 0.0010 0.0002
## 340 1.0800 nan 0.0010 0.0002
## 360 1.0696 nan 0.0010 0.0002
## 380 1.0590 nan 0.0010 0.0002
## 400 1.0489 nan 0.0010 0.0002
## 420 1.0390 nan 0.0010 0.0002
## 440 1.0296 nan 0.0010 0.0002
## 460 1.0201 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
## 500 1.0020 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3046 nan 0.0100 0.0038
## 3 1.2969 nan 0.0100 0.0037
## 4 1.2886 nan 0.0100 0.0036
## 5 1.2809 nan 0.0100 0.0039
## 6 1.2731 nan 0.0100 0.0039
## 7 1.2656 nan 0.0100 0.0034
## 8 1.2582 nan 0.0100 0.0034
## 9 1.2510 nan 0.0100 0.0031
## 10 1.2440 nan 0.0100 0.0033
## 20 1.1780 nan 0.0100 0.0032
## 40 1.0692 nan 0.0100 0.0020
## 60 0.9863 nan 0.0100 0.0012
## 80 0.9228 nan 0.0100 0.0012
## 100 0.8716 nan 0.0100 0.0009
## 120 0.8302 nan 0.0100 0.0005
## 140 0.7957 nan 0.0100 0.0002
## 160 0.7674 nan 0.0100 0.0006
## 180 0.7415 nan 0.0100 0.0003
## 200 0.7195 nan 0.0100 0.0001
## 220 0.7003 nan 0.0100 -0.0001
## 240 0.6829 nan 0.0100 0.0001
## 260 0.6663 nan 0.0100 -0.0000
## 280 0.6514 nan 0.0100 -0.0000
## 300 0.6387 nan 0.0100 -0.0001
## 320 0.6262 nan 0.0100 0.0000
## 340 0.6156 nan 0.0100 -0.0000
## 360 0.6058 nan 0.0100 0.0000
## 380 0.5948 nan 0.0100 -0.0001
## 400 0.5846 nan 0.0100 -0.0000
## 420 0.5751 nan 0.0100 0.0001
## 440 0.5666 nan 0.0100 0.0001
## 460 0.5578 nan 0.0100 -0.0002
## 480 0.5494 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3041 nan 0.0100 0.0038
## 3 1.2953 nan 0.0100 0.0037
## 4 1.2871 nan 0.0100 0.0037
## 5 1.2791 nan 0.0100 0.0037
## 6 1.2718 nan 0.0100 0.0035
## 7 1.2637 nan 0.0100 0.0035
## 8 1.2557 nan 0.0100 0.0037
## 9 1.2483 nan 0.0100 0.0033
## 10 1.2413 nan 0.0100 0.0034
## 20 1.1759 nan 0.0100 0.0024
## 40 1.0711 nan 0.0100 0.0023
## 60 0.9888 nan 0.0100 0.0012
## 80 0.9243 nan 0.0100 0.0010
## 100 0.8730 nan 0.0100 0.0009
## 120 0.8319 nan 0.0100 0.0009
## 140 0.7970 nan 0.0100 0.0003
## 160 0.7680 nan 0.0100 0.0005
## 180 0.7433 nan 0.0100 0.0003
## 200 0.7212 nan 0.0100 0.0002
## 220 0.7034 nan 0.0100 0.0001
## 240 0.6864 nan 0.0100 0.0002
## 260 0.6715 nan 0.0100 0.0001
## 280 0.6584 nan 0.0100 0.0000
## 300 0.6456 nan 0.0100 0.0000
## 320 0.6340 nan 0.0100 0.0000
## 340 0.6233 nan 0.0100 -0.0001
## 360 0.6132 nan 0.0100 0.0000
## 380 0.6030 nan 0.0100 -0.0001
## 400 0.5929 nan 0.0100 -0.0000
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## 440 0.5760 nan 0.0100 0.0000
## 460 0.5685 nan 0.0100 0.0000
## 480 0.5601 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3046 nan 0.0100 0.0035
## 3 1.2963 nan 0.0100 0.0037
## 4 1.2881 nan 0.0100 0.0038
## 5 1.2805 nan 0.0100 0.0031
## 6 1.2723 nan 0.0100 0.0037
## 7 1.2651 nan 0.0100 0.0033
## 8 1.2575 nan 0.0100 0.0033
## 9 1.2506 nan 0.0100 0.0031
## 10 1.2434 nan 0.0100 0.0029
## 20 1.1769 nan 0.0100 0.0029
## 40 1.0703 nan 0.0100 0.0021
## 60 0.9889 nan 0.0100 0.0015
## 80 0.9268 nan 0.0100 0.0013
## 100 0.8775 nan 0.0100 0.0006
## 120 0.8345 nan 0.0100 0.0008
## 140 0.8018 nan 0.0100 0.0002
## 160 0.7730 nan 0.0100 0.0000
## 180 0.7485 nan 0.0100 0.0003
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## 220 0.7080 nan 0.0100 0.0002
## 240 0.6928 nan 0.0100 0.0001
## 260 0.6776 nan 0.0100 0.0002
## 280 0.6637 nan 0.0100 0.0000
## 300 0.6513 nan 0.0100 0.0000
## 320 0.6405 nan 0.0100 0.0000
## 340 0.6300 nan 0.0100 -0.0001
## 360 0.6204 nan 0.0100 -0.0001
## 380 0.6102 nan 0.0100 -0.0001
## 400 0.6014 nan 0.0100 -0.0002
## 420 0.5917 nan 0.0100 0.0000
## 440 0.5828 nan 0.0100 -0.0000
## 460 0.5740 nan 0.0100 -0.0001
## 480 0.5648 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0036
## 2 1.3030 nan 0.0100 0.0038
## 3 1.2944 nan 0.0100 0.0037
## 4 1.2856 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0039
## 6 1.2690 nan 0.0100 0.0033
## 7 1.2602 nan 0.0100 0.0038
## 8 1.2516 nan 0.0100 0.0039
## 9 1.2434 nan 0.0100 0.0036
## 10 1.2360 nan 0.0100 0.0033
## 20 1.1668 nan 0.0100 0.0032
## 40 1.0563 nan 0.0100 0.0019
## 60 0.9712 nan 0.0100 0.0012
## 80 0.9033 nan 0.0100 0.0013
## 100 0.8493 nan 0.0100 0.0009
## 120 0.8056 nan 0.0100 0.0007
## 140 0.7695 nan 0.0100 0.0005
## 160 0.7392 nan 0.0100 0.0003
## 180 0.7120 nan 0.0100 0.0002
## 200 0.6889 nan 0.0100 0.0003
## 220 0.6681 nan 0.0100 0.0003
## 240 0.6495 nan 0.0100 -0.0000
## 260 0.6312 nan 0.0100 0.0001
## 280 0.6156 nan 0.0100 0.0000
## 300 0.6008 nan 0.0100 -0.0000
## 320 0.5876 nan 0.0100 -0.0001
## 340 0.5749 nan 0.0100 -0.0000
## 360 0.5633 nan 0.0100 -0.0000
## 380 0.5512 nan 0.0100 0.0001
## 400 0.5396 nan 0.0100 -0.0000
## 420 0.5294 nan 0.0100 -0.0001
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## 460 0.5090 nan 0.0100 -0.0000
## 480 0.5002 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0035
## 2 1.3037 nan 0.0100 0.0042
## 3 1.2947 nan 0.0100 0.0043
## 4 1.2865 nan 0.0100 0.0033
## 5 1.2789 nan 0.0100 0.0035
## 6 1.2707 nan 0.0100 0.0036
## 7 1.2625 nan 0.0100 0.0039
## 8 1.2552 nan 0.0100 0.0032
## 9 1.2475 nan 0.0100 0.0032
## 10 1.2400 nan 0.0100 0.0031
## 20 1.1696 nan 0.0100 0.0030
## 40 1.0545 nan 0.0100 0.0022
## 60 0.9682 nan 0.0100 0.0013
## 80 0.9008 nan 0.0100 0.0011
## 100 0.8472 nan 0.0100 0.0010
## 120 0.8037 nan 0.0100 0.0005
## 140 0.7672 nan 0.0100 0.0006
## 160 0.7375 nan 0.0100 0.0001
## 180 0.7113 nan 0.0100 0.0002
## 200 0.6904 nan 0.0100 0.0003
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## 260 0.6346 nan 0.0100 0.0000
## 280 0.6190 nan 0.0100 0.0000
## 300 0.6052 nan 0.0100 -0.0001
## 320 0.5906 nan 0.0100 0.0002
## 340 0.5779 nan 0.0100 0.0000
## 360 0.5668 nan 0.0100 0.0000
## 380 0.5555 nan 0.0100 -0.0001
## 400 0.5443 nan 0.0100 -0.0001
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## 440 0.5239 nan 0.0100 0.0001
## 460 0.5135 nan 0.0100 -0.0000
## 480 0.5045 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0040
## 2 1.3022 nan 0.0100 0.0036
## 3 1.2944 nan 0.0100 0.0036
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2776 nan 0.0100 0.0033
## 6 1.2704 nan 0.0100 0.0032
## 7 1.2621 nan 0.0100 0.0034
## 8 1.2543 nan 0.0100 0.0034
## 9 1.2465 nan 0.0100 0.0038
## 10 1.2394 nan 0.0100 0.0033
## 20 1.1711 nan 0.0100 0.0030
## 40 1.0624 nan 0.0100 0.0024
## 60 0.9794 nan 0.0100 0.0015
## 80 0.9110 nan 0.0100 0.0012
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## 120 0.8135 nan 0.0100 0.0006
## 140 0.7772 nan 0.0100 0.0003
## 160 0.7476 nan 0.0100 0.0004
## 180 0.7209 nan 0.0100 0.0003
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## 220 0.6794 nan 0.0100 0.0001
## 240 0.6613 nan 0.0100 0.0003
## 260 0.6450 nan 0.0100 0.0000
## 280 0.6302 nan 0.0100 -0.0001
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## 320 0.6044 nan 0.0100 -0.0000
## 340 0.5922 nan 0.0100 -0.0001
## 360 0.5808 nan 0.0100 -0.0000
## 380 0.5710 nan 0.0100 -0.0000
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## 460 0.5318 nan 0.0100 -0.0001
## 480 0.5220 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0044
## 2 1.3020 nan 0.0100 0.0039
## 3 1.2940 nan 0.0100 0.0036
## 4 1.2844 nan 0.0100 0.0043
## 5 1.2751 nan 0.0100 0.0043
## 6 1.2668 nan 0.0100 0.0039
## 7 1.2582 nan 0.0100 0.0040
## 8 1.2498 nan 0.0100 0.0034
## 9 1.2413 nan 0.0100 0.0040
## 10 1.2332 nan 0.0100 0.0036
## 20 1.1594 nan 0.0100 0.0031
## 40 1.0430 nan 0.0100 0.0020
## 60 0.9508 nan 0.0100 0.0016
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## 120 0.7788 nan 0.0100 0.0007
## 140 0.7406 nan 0.0100 0.0003
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## 280 0.5747 nan 0.0100 0.0002
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## 460 0.4633 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3103 nan 0.0100 0.0044
## 2 1.3016 nan 0.0100 0.0042
## 3 1.2922 nan 0.0100 0.0043
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2742 nan 0.0100 0.0043
## 6 1.2655 nan 0.0100 0.0041
## 7 1.2568 nan 0.0100 0.0040
## 8 1.2485 nan 0.0100 0.0031
## 9 1.2401 nan 0.0100 0.0037
## 10 1.2326 nan 0.0100 0.0031
## 20 1.1616 nan 0.0100 0.0028
## 40 1.0441 nan 0.0100 0.0019
## 60 0.9555 nan 0.0100 0.0017
## 80 0.8859 nan 0.0100 0.0012
## 100 0.8278 nan 0.0100 0.0009
## 120 0.7823 nan 0.0100 0.0005
## 140 0.7434 nan 0.0100 0.0003
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## 180 0.6853 nan 0.0100 0.0004
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## 240 0.6192 nan 0.0100 0.0001
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## 280 0.5851 nan 0.0100 0.0002
## 300 0.5703 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0040
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2862 nan 0.0100 0.0037
## 5 1.2771 nan 0.0100 0.0040
## 6 1.2684 nan 0.0100 0.0039
## 7 1.2604 nan 0.0100 0.0035
## 8 1.2520 nan 0.0100 0.0036
## 9 1.2452 nan 0.0100 0.0033
## 10 1.2373 nan 0.0100 0.0034
## 20 1.1655 nan 0.0100 0.0029
## 40 1.0512 nan 0.0100 0.0022
## 60 0.9640 nan 0.0100 0.0014
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## 100 0.8401 nan 0.0100 0.0010
## 120 0.7951 nan 0.0100 0.0008
## 140 0.7582 nan 0.0100 0.0004
## 160 0.7263 nan 0.0100 0.0003
## 180 0.6989 nan 0.0100 0.0003
## 200 0.6759 nan 0.0100 0.0001
## 220 0.6552 nan 0.0100 0.0001
## 240 0.6349 nan 0.0100 -0.0000
## 260 0.6179 nan 0.0100 0.0001
## 280 0.6022 nan 0.0100 -0.0000
## 300 0.5870 nan 0.0100 0.0001
## 320 0.5723 nan 0.0100 0.0001
## 340 0.5586 nan 0.0100 -0.0001
## 360 0.5459 nan 0.0100 0.0001
## 380 0.5333 nan 0.0100 0.0002
## 400 0.5210 nan 0.0100 -0.0000
## 420 0.5093 nan 0.0100 -0.0001
## 440 0.4979 nan 0.0100 0.0000
## 460 0.4869 nan 0.0100 0.0001
## 480 0.4771 nan 0.0100 -0.0001
## 500 0.4666 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2388 nan 0.1000 0.0382
## 2 1.1687 nan 0.1000 0.0291
## 3 1.1093 nan 0.1000 0.0271
## 4 1.0617 nan 0.1000 0.0211
## 5 1.0170 nan 0.1000 0.0172
## 6 0.9818 nan 0.1000 0.0127
## 7 0.9496 nan 0.1000 0.0123
## 8 0.9178 nan 0.1000 0.0119
## 9 0.8953 nan 0.1000 0.0053
## 10 0.8682 nan 0.1000 0.0116
## 20 0.7182 nan 0.1000 0.0017
## 40 0.5925 nan 0.1000 -0.0002
## 60 0.5183 nan 0.1000 -0.0007
## 80 0.4438 nan 0.1000 -0.0010
## 100 0.3936 nan 0.1000 -0.0001
## 120 0.3578 nan 0.1000 -0.0002
## 140 0.3196 nan 0.1000 -0.0008
## 160 0.2872 nan 0.1000 -0.0008
## 180 0.2562 nan 0.1000 -0.0001
## 200 0.2286 nan 0.1000 -0.0000
## 220 0.2058 nan 0.1000 0.0001
## 240 0.1866 nan 0.1000 -0.0006
## 260 0.1690 nan 0.1000 -0.0001
## 280 0.1538 nan 0.1000 -0.0003
## 300 0.1420 nan 0.1000 -0.0001
## 320 0.1294 nan 0.1000 -0.0002
## 340 0.1167 nan 0.1000 -0.0004
## 360 0.1065 nan 0.1000 -0.0004
## 380 0.0978 nan 0.1000 -0.0000
## 400 0.0904 nan 0.1000 -0.0001
## 420 0.0833 nan 0.1000 -0.0002
## 440 0.0775 nan 0.1000 -0.0001
## 460 0.0713 nan 0.1000 -0.0002
## 480 0.0657 nan 0.1000 -0.0002
## 500 0.0603 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2402 nan 0.1000 0.0364
## 2 1.1751 nan 0.1000 0.0302
## 3 1.1142 nan 0.1000 0.0263
## 4 1.0633 nan 0.1000 0.0221
## 5 1.0186 nan 0.1000 0.0178
## 6 0.9823 nan 0.1000 0.0146
## 7 0.9473 nan 0.1000 0.0137
## 8 0.9139 nan 0.1000 0.0128
## 9 0.8887 nan 0.1000 0.0092
## 10 0.8642 nan 0.1000 0.0112
## 20 0.7215 nan 0.1000 0.0001
## 40 0.5964 nan 0.1000 -0.0009
## 60 0.5168 nan 0.1000 -0.0008
## 80 0.4525 nan 0.1000 -0.0019
## 100 0.4022 nan 0.1000 -0.0012
## 120 0.3577 nan 0.1000 -0.0004
## 140 0.3258 nan 0.1000 -0.0002
## 160 0.2921 nan 0.1000 -0.0003
## 180 0.2600 nan 0.1000 -0.0004
## 200 0.2350 nan 0.1000 -0.0009
## 220 0.2131 nan 0.1000 -0.0004
## 240 0.1928 nan 0.1000 -0.0003
## 260 0.1755 nan 0.1000 -0.0003
## 280 0.1610 nan 0.1000 -0.0004
## 300 0.1464 nan 0.1000 -0.0003
## 320 0.1338 nan 0.1000 0.0003
## 340 0.1246 nan 0.1000 -0.0005
## 360 0.1135 nan 0.1000 -0.0002
## 380 0.1048 nan 0.1000 -0.0004
## 400 0.0958 nan 0.1000 -0.0001
## 420 0.0895 nan 0.1000 -0.0004
## 440 0.0825 nan 0.1000 0.0000
## 460 0.0759 nan 0.1000 -0.0002
## 480 0.0699 nan 0.1000 -0.0003
## 500 0.0635 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2382 nan 0.1000 0.0347
## 2 1.1718 nan 0.1000 0.0305
## 3 1.1166 nan 0.1000 0.0239
## 4 1.0610 nan 0.1000 0.0242
## 5 1.0176 nan 0.1000 0.0176
## 6 0.9864 nan 0.1000 0.0097
## 7 0.9560 nan 0.1000 0.0127
## 8 0.9293 nan 0.1000 0.0099
## 9 0.9029 nan 0.1000 0.0103
## 10 0.8837 nan 0.1000 0.0065
## 20 0.7272 nan 0.1000 0.0031
## 40 0.6028 nan 0.1000 -0.0032
## 60 0.5238 nan 0.1000 0.0004
## 80 0.4603 nan 0.1000 0.0011
## 100 0.4058 nan 0.1000 -0.0012
## 120 0.3628 nan 0.1000 -0.0004
## 140 0.3269 nan 0.1000 -0.0021
## 160 0.2920 nan 0.1000 -0.0009
## 180 0.2631 nan 0.1000 -0.0001
## 200 0.2392 nan 0.1000 -0.0005
## 220 0.2162 nan 0.1000 -0.0005
## 240 0.1951 nan 0.1000 -0.0005
## 260 0.1776 nan 0.1000 -0.0004
## 280 0.1607 nan 0.1000 -0.0004
## 300 0.1482 nan 0.1000 -0.0006
## 320 0.1373 nan 0.1000 -0.0006
## 340 0.1264 nan 0.1000 -0.0006
## 360 0.1165 nan 0.1000 -0.0002
## 380 0.1064 nan 0.1000 -0.0001
## 400 0.0987 nan 0.1000 -0.0002
## 420 0.0919 nan 0.1000 -0.0004
## 440 0.0848 nan 0.1000 -0.0003
## 460 0.0785 nan 0.1000 -0.0001
## 480 0.0729 nan 0.1000 -0.0001
## 500 0.0682 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0401
## 2 1.1668 nan 0.1000 0.0276
## 3 1.1058 nan 0.1000 0.0220
## 4 1.0497 nan 0.1000 0.0232
## 5 1.0063 nan 0.1000 0.0170
## 6 0.9695 nan 0.1000 0.0128
## 7 0.9327 nan 0.1000 0.0151
## 8 0.9010 nan 0.1000 0.0126
## 9 0.8719 nan 0.1000 0.0108
## 10 0.8454 nan 0.1000 0.0100
## 20 0.6955 nan 0.1000 -0.0002
## 40 0.5435 nan 0.1000 0.0001
## 60 0.4553 nan 0.1000 -0.0008
## 80 0.3799 nan 0.1000 -0.0006
## 100 0.3265 nan 0.1000 0.0005
## 120 0.2851 nan 0.1000 0.0002
## 140 0.2486 nan 0.1000 -0.0008
## 160 0.2181 nan 0.1000 -0.0001
## 180 0.1921 nan 0.1000 -0.0005
## 200 0.1685 nan 0.1000 -0.0005
## 220 0.1483 nan 0.1000 -0.0002
## 240 0.1312 nan 0.1000 -0.0005
## 260 0.1173 nan 0.1000 -0.0001
## 280 0.1025 nan 0.1000 -0.0001
## 300 0.0912 nan 0.1000 -0.0001
## 320 0.0815 nan 0.1000 -0.0001
## 340 0.0738 nan 0.1000 -0.0002
## 360 0.0663 nan 0.1000 -0.0002
## 380 0.0605 nan 0.1000 -0.0001
## 400 0.0538 nan 0.1000 -0.0001
## 420 0.0484 nan 0.1000 -0.0001
## 440 0.0439 nan 0.1000 -0.0002
## 460 0.0396 nan 0.1000 -0.0000
## 480 0.0355 nan 0.1000 -0.0001
## 500 0.0323 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2343 nan 0.1000 0.0394
## 2 1.1589 nan 0.1000 0.0332
## 3 1.1023 nan 0.1000 0.0266
## 4 1.0439 nan 0.1000 0.0236
## 5 0.9985 nan 0.1000 0.0187
## 6 0.9624 nan 0.1000 0.0154
## 7 0.9253 nan 0.1000 0.0148
## 8 0.8961 nan 0.1000 0.0127
## 9 0.8620 nan 0.1000 0.0129
## 10 0.8350 nan 0.1000 0.0115
## 20 0.6897 nan 0.1000 0.0039
## 40 0.5480 nan 0.1000 -0.0001
## 60 0.4652 nan 0.1000 0.0002
## 80 0.3909 nan 0.1000 -0.0007
## 100 0.3384 nan 0.1000 -0.0013
## 120 0.2911 nan 0.1000 -0.0009
## 140 0.2543 nan 0.1000 -0.0006
## 160 0.2229 nan 0.1000 -0.0007
## 180 0.1966 nan 0.1000 -0.0004
## 200 0.1732 nan 0.1000 -0.0007
## 220 0.1556 nan 0.1000 -0.0006
## 240 0.1397 nan 0.1000 -0.0005
## 260 0.1245 nan 0.1000 -0.0007
## 280 0.1125 nan 0.1000 -0.0002
## 300 0.1008 nan 0.1000 -0.0004
## 320 0.0899 nan 0.1000 -0.0005
## 340 0.0810 nan 0.1000 -0.0004
## 360 0.0727 nan 0.1000 -0.0002
## 380 0.0657 nan 0.1000 -0.0001
## 400 0.0597 nan 0.1000 -0.0002
## 420 0.0537 nan 0.1000 -0.0000
## 440 0.0484 nan 0.1000 -0.0000
## 460 0.0436 nan 0.1000 -0.0002
## 480 0.0391 nan 0.1000 -0.0003
## 500 0.0353 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0337
## 2 1.1764 nan 0.1000 0.0321
## 3 1.1071 nan 0.1000 0.0304
## 4 1.0576 nan 0.1000 0.0220
## 5 1.0138 nan 0.1000 0.0162
## 6 0.9752 nan 0.1000 0.0147
## 7 0.9395 nan 0.1000 0.0125
## 8 0.9079 nan 0.1000 0.0138
## 9 0.8780 nan 0.1000 0.0102
## 10 0.8508 nan 0.1000 0.0105
## 20 0.7052 nan 0.1000 0.0008
## 40 0.5637 nan 0.1000 -0.0000
## 60 0.4737 nan 0.1000 0.0001
## 80 0.4056 nan 0.1000 -0.0006
## 100 0.3529 nan 0.1000 -0.0007
## 120 0.3055 nan 0.1000 -0.0005
## 140 0.2683 nan 0.1000 -0.0009
## 160 0.2381 nan 0.1000 -0.0003
## 180 0.2113 nan 0.1000 -0.0014
## 200 0.1881 nan 0.1000 -0.0009
## 220 0.1704 nan 0.1000 -0.0012
## 240 0.1510 nan 0.1000 -0.0012
## 260 0.1359 nan 0.1000 -0.0006
## 280 0.1198 nan 0.1000 -0.0005
## 300 0.1076 nan 0.1000 -0.0008
## 320 0.0957 nan 0.1000 -0.0002
## 340 0.0850 nan 0.1000 -0.0001
## 360 0.0767 nan 0.1000 -0.0006
## 380 0.0693 nan 0.1000 -0.0002
## 400 0.0624 nan 0.1000 -0.0001
## 420 0.0564 nan 0.1000 -0.0002
## 440 0.0510 nan 0.1000 -0.0001
## 460 0.0452 nan 0.1000 -0.0001
## 480 0.0414 nan 0.1000 -0.0002
## 500 0.0375 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.1000 0.0407
## 2 1.1506 nan 0.1000 0.0322
## 3 1.0852 nan 0.1000 0.0296
## 4 1.0261 nan 0.1000 0.0241
## 5 0.9796 nan 0.1000 0.0198
## 6 0.9391 nan 0.1000 0.0160
## 7 0.9009 nan 0.1000 0.0155
## 8 0.8721 nan 0.1000 0.0107
## 9 0.8452 nan 0.1000 0.0077
## 10 0.8176 nan 0.1000 0.0081
## 20 0.6542 nan 0.1000 0.0026
## 40 0.5088 nan 0.1000 -0.0005
## 60 0.4113 nan 0.1000 -0.0011
## 80 0.3361 nan 0.1000 -0.0011
## 100 0.2860 nan 0.1000 -0.0004
## 120 0.2418 nan 0.1000 -0.0001
## 140 0.2055 nan 0.1000 0.0002
## 160 0.1771 nan 0.1000 -0.0006
## 180 0.1503 nan 0.1000 -0.0001
## 200 0.1298 nan 0.1000 -0.0004
## 220 0.1128 nan 0.1000 -0.0003
## 240 0.0986 nan 0.1000 -0.0004
## 260 0.0846 nan 0.1000 -0.0003
## 280 0.0740 nan 0.1000 -0.0001
## 300 0.0646 nan 0.1000 -0.0001
## 320 0.0563 nan 0.1000 -0.0001
## 340 0.0495 nan 0.1000 -0.0000
## 360 0.0437 nan 0.1000 -0.0001
## 380 0.0386 nan 0.1000 -0.0001
## 400 0.0342 nan 0.1000 -0.0001
## 420 0.0301 nan 0.1000 -0.0001
## 440 0.0266 nan 0.1000 -0.0001
## 460 0.0237 nan 0.1000 -0.0000
## 480 0.0208 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0375
## 2 1.1630 nan 0.1000 0.0329
## 3 1.1047 nan 0.1000 0.0271
## 4 1.0466 nan 0.1000 0.0279
## 5 0.9928 nan 0.1000 0.0218
## 6 0.9458 nan 0.1000 0.0192
## 7 0.9101 nan 0.1000 0.0136
## 8 0.8782 nan 0.1000 0.0107
## 9 0.8472 nan 0.1000 0.0114
## 10 0.8229 nan 0.1000 0.0097
## 20 0.6627 nan 0.1000 0.0015
## 40 0.5041 nan 0.1000 0.0004
## 60 0.4121 nan 0.1000 -0.0004
## 80 0.3435 nan 0.1000 -0.0011
## 100 0.2873 nan 0.1000 0.0001
## 120 0.2453 nan 0.1000 -0.0012
## 140 0.2119 nan 0.1000 -0.0004
## 160 0.1844 nan 0.1000 -0.0008
## 180 0.1581 nan 0.1000 -0.0003
## 200 0.1380 nan 0.1000 -0.0003
## 220 0.1193 nan 0.1000 -0.0003
## 240 0.1043 nan 0.1000 -0.0001
## 260 0.0901 nan 0.1000 -0.0001
## 280 0.0785 nan 0.1000 -0.0002
## 300 0.0692 nan 0.1000 -0.0002
## 320 0.0604 nan 0.1000 -0.0001
## 340 0.0527 nan 0.1000 -0.0001
## 360 0.0468 nan 0.1000 -0.0002
## 380 0.0410 nan 0.1000 -0.0001
## 400 0.0361 nan 0.1000 -0.0000
## 420 0.0319 nan 0.1000 -0.0001
## 440 0.0281 nan 0.1000 -0.0001
## 460 0.0249 nan 0.1000 -0.0000
## 480 0.0218 nan 0.1000 -0.0000
## 500 0.0191 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2309 nan 0.1000 0.0405
## 2 1.1600 nan 0.1000 0.0298
## 3 1.1009 nan 0.1000 0.0243
## 4 1.0436 nan 0.1000 0.0293
## 5 0.9930 nan 0.1000 0.0208
## 6 0.9514 nan 0.1000 0.0193
## 7 0.9212 nan 0.1000 0.0110
## 8 0.8878 nan 0.1000 0.0122
## 9 0.8601 nan 0.1000 0.0126
## 10 0.8376 nan 0.1000 0.0080
## 20 0.6740 nan 0.1000 0.0017
## 40 0.5316 nan 0.1000 0.0006
## 60 0.4325 nan 0.1000 -0.0009
## 80 0.3571 nan 0.1000 0.0005
## 100 0.3029 nan 0.1000 -0.0006
## 120 0.2578 nan 0.1000 -0.0004
## 140 0.2245 nan 0.1000 -0.0018
## 160 0.1924 nan 0.1000 -0.0005
## 180 0.1682 nan 0.1000 -0.0004
## 200 0.1489 nan 0.1000 -0.0005
## 220 0.1317 nan 0.1000 -0.0006
## 240 0.1138 nan 0.1000 -0.0007
## 260 0.0998 nan 0.1000 -0.0004
## 280 0.0886 nan 0.1000 -0.0001
## 300 0.0770 nan 0.1000 -0.0003
## 320 0.0677 nan 0.1000 -0.0002
## 340 0.0600 nan 0.1000 -0.0001
## 360 0.0529 nan 0.1000 -0.0001
## 380 0.0474 nan 0.1000 -0.0002
## 400 0.0422 nan 0.1000 -0.0002
## 420 0.0368 nan 0.1000 -0.0001
## 440 0.0325 nan 0.1000 -0.0001
## 460 0.0295 nan 0.1000 -0.0002
## 480 0.0259 nan 0.1000 -0.0002
## 500 0.0227 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2715 nan 0.0010 0.0004
## 80 1.2565 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2276 nan 0.0010 0.0003
## 140 1.2138 nan 0.0010 0.0003
## 160 1.2003 nan 0.0010 0.0003
## 180 1.1874 nan 0.0010 0.0003
## 200 1.1748 nan 0.0010 0.0002
## 220 1.1630 nan 0.0010 0.0003
## 240 1.1512 nan 0.0010 0.0002
## 260 1.1395 nan 0.0010 0.0003
## 280 1.1284 nan 0.0010 0.0002
## 300 1.1175 nan 0.0010 0.0003
## 320 1.1072 nan 0.0010 0.0002
## 340 1.0970 nan 0.0010 0.0002
## 360 1.0872 nan 0.0010 0.0002
## 380 1.0777 nan 0.0010 0.0002
## 400 1.0685 nan 0.0010 0.0002
## 420 1.0593 nan 0.0010 0.0002
## 440 1.0504 nan 0.0010 0.0002
## 460 1.0416 nan 0.0010 0.0002
## 480 1.0331 nan 0.0010 0.0002
## 500 1.0249 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2725 nan 0.0010 0.0004
## 80 1.2572 nan 0.0010 0.0004
## 100 1.2428 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2019 nan 0.0010 0.0003
## 180 1.1890 nan 0.0010 0.0003
## 200 1.1764 nan 0.0010 0.0003
## 220 1.1641 nan 0.0010 0.0003
## 240 1.1525 nan 0.0010 0.0003
## 260 1.1412 nan 0.0010 0.0002
## 280 1.1300 nan 0.0010 0.0002
## 300 1.1194 nan 0.0010 0.0002
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0985 nan 0.0010 0.0002
## 360 1.0885 nan 0.0010 0.0002
## 380 1.0789 nan 0.0010 0.0002
## 400 1.0699 nan 0.0010 0.0001
## 420 1.0607 nan 0.0010 0.0002
## 440 1.0518 nan 0.0010 0.0002
## 460 1.0432 nan 0.0010 0.0002
## 480 1.0348 nan 0.0010 0.0002
## 500 1.0266 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2879 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2021 nan 0.0010 0.0002
## 180 1.1893 nan 0.0010 0.0002
## 200 1.1768 nan 0.0010 0.0003
## 220 1.1645 nan 0.0010 0.0003
## 240 1.1530 nan 0.0010 0.0002
## 260 1.1415 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1199 nan 0.0010 0.0002
## 320 1.1096 nan 0.0010 0.0002
## 340 1.0995 nan 0.0010 0.0002
## 360 1.0898 nan 0.0010 0.0002
## 380 1.0803 nan 0.0010 0.0002
## 400 1.0710 nan 0.0010 0.0002
## 420 1.0620 nan 0.0010 0.0002
## 440 1.0533 nan 0.0010 0.0002
## 460 1.0448 nan 0.0010 0.0002
## 480 1.0363 nan 0.0010 0.0002
## 500 1.0281 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2218 nan 0.0010 0.0003
## 140 1.2070 nan 0.0010 0.0003
## 160 1.1927 nan 0.0010 0.0003
## 180 1.1794 nan 0.0010 0.0003
## 200 1.1660 nan 0.0010 0.0003
## 220 1.1529 nan 0.0010 0.0003
## 240 1.1406 nan 0.0010 0.0003
## 260 1.1286 nan 0.0010 0.0002
## 280 1.1169 nan 0.0010 0.0003
## 300 1.1055 nan 0.0010 0.0002
## 320 1.0943 nan 0.0010 0.0003
## 340 1.0837 nan 0.0010 0.0002
## 360 1.0734 nan 0.0010 0.0002
## 380 1.0632 nan 0.0010 0.0002
## 400 1.0532 nan 0.0010 0.0002
## 420 1.0438 nan 0.0010 0.0002
## 440 1.0344 nan 0.0010 0.0002
## 460 1.0252 nan 0.0010 0.0002
## 480 1.0166 nan 0.0010 0.0002
## 500 1.0080 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0003
## 80 1.2533 nan 0.0010 0.0003
## 100 1.2379 nan 0.0010 0.0003
## 120 1.2230 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0003
## 160 1.1948 nan 0.0010 0.0003
## 180 1.1811 nan 0.0010 0.0003
## 200 1.1680 nan 0.0010 0.0002
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1430 nan 0.0010 0.0003
## 260 1.1311 nan 0.0010 0.0003
## 280 1.1193 nan 0.0010 0.0003
## 300 1.1079 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0863 nan 0.0010 0.0002
## 360 1.0756 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0554 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
## 440 1.0365 nan 0.0010 0.0002
## 460 1.0276 nan 0.0010 0.0002
## 480 1.0190 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2394 nan 0.0010 0.0003
## 120 1.2250 nan 0.0010 0.0003
## 140 1.2107 nan 0.0010 0.0003
## 160 1.1969 nan 0.0010 0.0003
## 180 1.1834 nan 0.0010 0.0003
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1578 nan 0.0010 0.0003
## 240 1.1455 nan 0.0010 0.0003
## 260 1.1338 nan 0.0010 0.0003
## 280 1.1219 nan 0.0010 0.0003
## 300 1.1106 nan 0.0010 0.0002
## 320 1.0998 nan 0.0010 0.0002
## 340 1.0893 nan 0.0010 0.0003
## 360 1.0789 nan 0.0010 0.0002
## 380 1.0689 nan 0.0010 0.0002
## 400 1.0592 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0002
## 440 1.0403 nan 0.0010 0.0002
## 460 1.0313 nan 0.0010 0.0002
## 480 1.0225 nan 0.0010 0.0002
## 500 1.0140 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0003
## 80 1.2497 nan 0.0010 0.0003
## 100 1.2336 nan 0.0010 0.0004
## 120 1.2181 nan 0.0010 0.0003
## 140 1.2029 nan 0.0010 0.0003
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1738 nan 0.0010 0.0003
## 200 1.1598 nan 0.0010 0.0003
## 220 1.1467 nan 0.0010 0.0003
## 240 1.1337 nan 0.0010 0.0003
## 260 1.1214 nan 0.0010 0.0003
## 280 1.1088 nan 0.0010 0.0003
## 300 1.0968 nan 0.0010 0.0003
## 320 1.0850 nan 0.0010 0.0003
## 340 1.0739 nan 0.0010 0.0002
## 360 1.0630 nan 0.0010 0.0002
## 380 1.0524 nan 0.0010 0.0002
## 400 1.0421 nan 0.0010 0.0002
## 420 1.0319 nan 0.0010 0.0002
## 440 1.0221 nan 0.0010 0.0002
## 460 1.0127 nan 0.0010 0.0002
## 480 1.0037 nan 0.0010 0.0002
## 500 0.9948 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2501 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0004
## 120 1.2185 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1886 nan 0.0010 0.0003
## 180 1.1749 nan 0.0010 0.0003
## 200 1.1611 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1225 nan 0.0010 0.0003
## 280 1.1105 nan 0.0010 0.0003
## 300 1.0988 nan 0.0010 0.0002
## 320 1.0874 nan 0.0010 0.0003
## 340 1.0766 nan 0.0010 0.0002
## 360 1.0658 nan 0.0010 0.0002
## 380 1.0551 nan 0.0010 0.0002
## 400 1.0450 nan 0.0010 0.0002
## 420 1.0350 nan 0.0010 0.0002
## 440 1.0255 nan 0.0010 0.0002
## 460 1.0161 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
## 500 0.9978 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2360 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0003
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1909 nan 0.0010 0.0003
## 180 1.1774 nan 0.0010 0.0003
## 200 1.1641 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1384 nan 0.0010 0.0003
## 260 1.1261 nan 0.0010 0.0002
## 280 1.1142 nan 0.0010 0.0003
## 300 1.1025 nan 0.0010 0.0002
## 320 1.0911 nan 0.0010 0.0002
## 340 1.0802 nan 0.0010 0.0002
## 360 1.0696 nan 0.0010 0.0002
## 380 1.0593 nan 0.0010 0.0002
## 400 1.0494 nan 0.0010 0.0002
## 420 1.0398 nan 0.0010 0.0002
## 440 1.0303 nan 0.0010 0.0002
## 460 1.0210 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0035 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3049 nan 0.0100 0.0037
## 3 1.2969 nan 0.0100 0.0039
## 4 1.2888 nan 0.0100 0.0034
## 5 1.2806 nan 0.0100 0.0037
## 6 1.2719 nan 0.0100 0.0041
## 7 1.2643 nan 0.0100 0.0032
## 8 1.2569 nan 0.0100 0.0033
## 9 1.2502 nan 0.0100 0.0032
## 10 1.2433 nan 0.0100 0.0030
## 20 1.1760 nan 0.0100 0.0025
## 40 1.0683 nan 0.0100 0.0021
## 60 0.9871 nan 0.0100 0.0015
## 80 0.9241 nan 0.0100 0.0009
## 100 0.8714 nan 0.0100 0.0009
## 120 0.8295 nan 0.0100 0.0006
## 140 0.7950 nan 0.0100 0.0003
## 160 0.7657 nan 0.0100 0.0004
## 180 0.7411 nan 0.0100 0.0004
## 200 0.7176 nan 0.0100 0.0001
## 220 0.6976 nan 0.0100 0.0001
## 240 0.6802 nan 0.0100 0.0001
## 260 0.6652 nan 0.0100 0.0001
## 280 0.6519 nan 0.0100 0.0001
## 300 0.6376 nan 0.0100 -0.0000
## 320 0.6249 nan 0.0100 -0.0001
## 340 0.6132 nan 0.0100 0.0001
## 360 0.6028 nan 0.0100 0.0000
## 380 0.5918 nan 0.0100 0.0001
## 400 0.5818 nan 0.0100 -0.0000
## 420 0.5723 nan 0.0100 -0.0000
## 440 0.5634 nan 0.0100 -0.0000
## 460 0.5545 nan 0.0100 0.0000
## 480 0.5465 nan 0.0100 0.0001
## 500 0.5392 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0038
## 3 1.2952 nan 0.0100 0.0038
## 4 1.2870 nan 0.0100 0.0039
## 5 1.2789 nan 0.0100 0.0039
## 6 1.2712 nan 0.0100 0.0039
## 7 1.2637 nan 0.0100 0.0031
## 8 1.2563 nan 0.0100 0.0034
## 9 1.2493 nan 0.0100 0.0031
## 10 1.2419 nan 0.0100 0.0032
## 20 1.1761 nan 0.0100 0.0027
## 40 1.0692 nan 0.0100 0.0020
## 60 0.9864 nan 0.0100 0.0014
## 80 0.9236 nan 0.0100 0.0012
## 100 0.8723 nan 0.0100 0.0008
## 120 0.8307 nan 0.0100 0.0009
## 140 0.7968 nan 0.0100 0.0005
## 160 0.7672 nan 0.0100 0.0003
## 180 0.7420 nan 0.0100 0.0003
## 200 0.7196 nan 0.0100 0.0003
## 220 0.7016 nan 0.0100 0.0002
## 240 0.6856 nan 0.0100 0.0002
## 260 0.6710 nan 0.0100 0.0002
## 280 0.6567 nan 0.0100 0.0001
## 300 0.6445 nan 0.0100 0.0001
## 320 0.6336 nan 0.0100 -0.0000
## 340 0.6224 nan 0.0100 0.0001
## 360 0.6102 nan 0.0100 -0.0000
## 380 0.6010 nan 0.0100 -0.0001
## 400 0.5909 nan 0.0100 0.0002
## 420 0.5813 nan 0.0100 -0.0000
## 440 0.5719 nan 0.0100 -0.0001
## 460 0.5628 nan 0.0100 -0.0001
## 480 0.5548 nan 0.0100 -0.0001
## 500 0.5458 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0035
## 2 1.3047 nan 0.0100 0.0039
## 3 1.2964 nan 0.0100 0.0039
## 4 1.2887 nan 0.0100 0.0033
## 5 1.2816 nan 0.0100 0.0033
## 6 1.2735 nan 0.0100 0.0037
## 7 1.2659 nan 0.0100 0.0036
## 8 1.2592 nan 0.0100 0.0033
## 9 1.2518 nan 0.0100 0.0033
## 10 1.2455 nan 0.0100 0.0031
## 20 1.1811 nan 0.0100 0.0031
## 40 1.0736 nan 0.0100 0.0021
## 60 0.9917 nan 0.0100 0.0015
## 80 0.9290 nan 0.0100 0.0012
## 100 0.8779 nan 0.0100 0.0007
## 120 0.8348 nan 0.0100 0.0008
## 140 0.8005 nan 0.0100 0.0006
## 160 0.7711 nan 0.0100 0.0005
## 180 0.7469 nan 0.0100 0.0003
## 200 0.7257 nan 0.0100 0.0003
## 220 0.7070 nan 0.0100 0.0003
## 240 0.6907 nan 0.0100 0.0000
## 260 0.6745 nan 0.0100 0.0001
## 280 0.6613 nan 0.0100 0.0001
## 300 0.6484 nan 0.0100 0.0000
## 320 0.6372 nan 0.0100 0.0000
## 340 0.6257 nan 0.0100 0.0001
## 360 0.6151 nan 0.0100 -0.0000
## 380 0.6051 nan 0.0100 -0.0001
## 400 0.5951 nan 0.0100 -0.0001
## 420 0.5855 nan 0.0100 0.0001
## 440 0.5769 nan 0.0100 0.0001
## 460 0.5684 nan 0.0100 -0.0001
## 480 0.5609 nan 0.0100 0.0000
## 500 0.5523 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2941 nan 0.0100 0.0035
## 4 1.2853 nan 0.0100 0.0042
## 5 1.2770 nan 0.0100 0.0035
## 6 1.2697 nan 0.0100 0.0034
## 7 1.2615 nan 0.0100 0.0037
## 8 1.2535 nan 0.0100 0.0037
## 9 1.2458 nan 0.0100 0.0036
## 10 1.2381 nan 0.0100 0.0034
## 20 1.1665 nan 0.0100 0.0030
## 40 1.0527 nan 0.0100 0.0021
## 60 0.9678 nan 0.0100 0.0013
## 80 0.9002 nan 0.0100 0.0010
## 100 0.8461 nan 0.0100 0.0008
## 120 0.8025 nan 0.0100 0.0003
## 140 0.7666 nan 0.0100 0.0004
## 160 0.7349 nan 0.0100 0.0005
## 180 0.7082 nan 0.0100 0.0001
## 200 0.6841 nan 0.0100 0.0001
## 220 0.6632 nan 0.0100 0.0003
## 240 0.6453 nan 0.0100 0.0002
## 260 0.6281 nan 0.0100 0.0002
## 280 0.6111 nan 0.0100 0.0001
## 300 0.5970 nan 0.0100 0.0001
## 320 0.5831 nan 0.0100 0.0001
## 340 0.5706 nan 0.0100 0.0001
## 360 0.5586 nan 0.0100 0.0001
## 380 0.5471 nan 0.0100 -0.0000
## 400 0.5364 nan 0.0100 -0.0001
## 420 0.5270 nan 0.0100 0.0000
## 440 0.5167 nan 0.0100 0.0000
## 460 0.5068 nan 0.0100 -0.0000
## 480 0.4976 nan 0.0100 -0.0001
## 500 0.4879 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3032 nan 0.0100 0.0038
## 3 1.2941 nan 0.0100 0.0042
## 4 1.2859 nan 0.0100 0.0036
## 5 1.2774 nan 0.0100 0.0040
## 6 1.2690 nan 0.0100 0.0036
## 7 1.2602 nan 0.0100 0.0040
## 8 1.2519 nan 0.0100 0.0040
## 9 1.2439 nan 0.0100 0.0035
## 10 1.2361 nan 0.0100 0.0030
## 20 1.1680 nan 0.0100 0.0024
## 40 1.0547 nan 0.0100 0.0022
## 60 0.9700 nan 0.0100 0.0015
## 80 0.9026 nan 0.0100 0.0012
## 100 0.8493 nan 0.0100 0.0009
## 120 0.8046 nan 0.0100 0.0007
## 140 0.7685 nan 0.0100 0.0006
## 160 0.7377 nan 0.0100 0.0004
## 180 0.7110 nan 0.0100 0.0003
## 200 0.6885 nan 0.0100 0.0004
## 220 0.6672 nan 0.0100 0.0000
## 240 0.6494 nan 0.0100 0.0002
## 260 0.6330 nan 0.0100 0.0000
## 280 0.6179 nan 0.0100 -0.0001
## 300 0.6055 nan 0.0100 0.0001
## 320 0.5928 nan 0.0100 0.0000
## 340 0.5796 nan 0.0100 0.0000
## 360 0.5671 nan 0.0100 -0.0000
## 380 0.5564 nan 0.0100 0.0001
## 400 0.5461 nan 0.0100 -0.0000
## 420 0.5366 nan 0.0100 -0.0002
## 440 0.5263 nan 0.0100 0.0000
## 460 0.5170 nan 0.0100 0.0001
## 480 0.5075 nan 0.0100 -0.0000
## 500 0.4987 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3026 nan 0.0100 0.0039
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2856 nan 0.0100 0.0042
## 5 1.2772 nan 0.0100 0.0040
## 6 1.2689 nan 0.0100 0.0041
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2531 nan 0.0100 0.0036
## 9 1.2455 nan 0.0100 0.0032
## 10 1.2381 nan 0.0100 0.0035
## 20 1.1700 nan 0.0100 0.0029
## 40 1.0587 nan 0.0100 0.0019
## 60 0.9729 nan 0.0100 0.0013
## 80 0.9059 nan 0.0100 0.0013
## 100 0.8523 nan 0.0100 0.0006
## 120 0.8083 nan 0.0100 0.0004
## 140 0.7724 nan 0.0100 0.0007
## 160 0.7412 nan 0.0100 0.0005
## 180 0.7152 nan 0.0100 -0.0001
## 200 0.6921 nan 0.0100 0.0002
## 220 0.6712 nan 0.0100 0.0003
## 240 0.6530 nan 0.0100 -0.0001
## 260 0.6378 nan 0.0100 0.0001
## 280 0.6226 nan 0.0100 -0.0000
## 300 0.6095 nan 0.0100 0.0001
## 320 0.5971 nan 0.0100 -0.0002
## 340 0.5830 nan 0.0100 -0.0000
## 360 0.5706 nan 0.0100 -0.0001
## 380 0.5603 nan 0.0100 -0.0000
## 400 0.5489 nan 0.0100 0.0001
## 420 0.5386 nan 0.0100 -0.0001
## 440 0.5291 nan 0.0100 0.0000
## 460 0.5201 nan 0.0100 0.0000
## 480 0.5113 nan 0.0100 -0.0001
## 500 0.5027 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0041
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2927 nan 0.0100 0.0042
## 4 1.2842 nan 0.0100 0.0041
## 5 1.2762 nan 0.0100 0.0034
## 6 1.2672 nan 0.0100 0.0039
## 7 1.2587 nan 0.0100 0.0040
## 8 1.2504 nan 0.0100 0.0038
## 9 1.2425 nan 0.0100 0.0036
## 10 1.2339 nan 0.0100 0.0037
## 20 1.1602 nan 0.0100 0.0028
## 40 1.0430 nan 0.0100 0.0020
## 60 0.9540 nan 0.0100 0.0016
## 80 0.8839 nan 0.0100 0.0010
## 100 0.8287 nan 0.0100 0.0009
## 120 0.7829 nan 0.0100 0.0006
## 140 0.7430 nan 0.0100 0.0005
## 160 0.7100 nan 0.0100 0.0003
## 180 0.6816 nan 0.0100 0.0002
## 200 0.6566 nan 0.0100 0.0001
## 220 0.6339 nan 0.0100 0.0000
## 240 0.6130 nan 0.0100 0.0000
## 260 0.5939 nan 0.0100 0.0000
## 280 0.5769 nan 0.0100 0.0001
## 300 0.5621 nan 0.0100 0.0000
## 320 0.5483 nan 0.0100 -0.0000
## 340 0.5333 nan 0.0100 0.0001
## 360 0.5201 nan 0.0100 -0.0000
## 380 0.5073 nan 0.0100 0.0000
## 400 0.4955 nan 0.0100 0.0001
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## 440 0.4740 nan 0.0100 0.0000
## 460 0.4641 nan 0.0100 0.0001
## 480 0.4543 nan 0.0100 -0.0003
## 500 0.4447 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0042
## 2 1.3015 nan 0.0100 0.0046
## 3 1.2923 nan 0.0100 0.0042
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2742 nan 0.0100 0.0038
## 6 1.2660 nan 0.0100 0.0040
## 7 1.2578 nan 0.0100 0.0037
## 8 1.2498 nan 0.0100 0.0037
## 9 1.2414 nan 0.0100 0.0040
## 10 1.2337 nan 0.0100 0.0036
## 20 1.1602 nan 0.0100 0.0030
## 40 1.0442 nan 0.0100 0.0018
## 60 0.9556 nan 0.0100 0.0017
## 80 0.8865 nan 0.0100 0.0013
## 100 0.8326 nan 0.0100 0.0008
## 120 0.7860 nan 0.0100 0.0007
## 140 0.7476 nan 0.0100 0.0006
## 160 0.7151 nan 0.0100 0.0004
## 180 0.6870 nan 0.0100 0.0003
## 200 0.6629 nan 0.0100 0.0000
## 220 0.6408 nan 0.0100 0.0002
## 240 0.6206 nan 0.0100 0.0001
## 260 0.6024 nan 0.0100 0.0001
## 280 0.5843 nan 0.0100 0.0002
## 300 0.5680 nan 0.0100 0.0001
## 320 0.5538 nan 0.0100 0.0000
## 340 0.5392 nan 0.0100 -0.0000
## 360 0.5260 nan 0.0100 0.0001
## 380 0.5148 nan 0.0100 0.0001
## 400 0.5018 nan 0.0100 0.0000
## 420 0.4902 nan 0.0100 0.0000
## 440 0.4786 nan 0.0100 -0.0001
## 460 0.4682 nan 0.0100 -0.0001
## 480 0.4580 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0039
## 3 1.2936 nan 0.0100 0.0035
## 4 1.2849 nan 0.0100 0.0042
## 5 1.2759 nan 0.0100 0.0038
## 6 1.2665 nan 0.0100 0.0044
## 7 1.2585 nan 0.0100 0.0037
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2419 nan 0.0100 0.0039
## 10 1.2341 nan 0.0100 0.0038
## 20 1.1620 nan 0.0100 0.0027
## 40 1.0472 nan 0.0100 0.0021
## 60 0.9606 nan 0.0100 0.0017
## 80 0.8927 nan 0.0100 0.0013
## 100 0.8378 nan 0.0100 0.0009
## 120 0.7948 nan 0.0100 0.0007
## 140 0.7575 nan 0.0100 0.0006
## 160 0.7250 nan 0.0100 0.0003
## 180 0.6976 nan 0.0100 0.0002
## 200 0.6729 nan 0.0100 0.0003
## 220 0.6503 nan 0.0100 0.0002
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## 260 0.6126 nan 0.0100 0.0000
## 280 0.5961 nan 0.0100 0.0001
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## 320 0.5664 nan 0.0100 0.0001
## 340 0.5521 nan 0.0100 0.0001
## 360 0.5389 nan 0.0100 -0.0000
## 380 0.5266 nan 0.0100 0.0001
## 400 0.5143 nan 0.0100 0.0002
## 420 0.5034 nan 0.0100 -0.0001
## 440 0.4923 nan 0.0100 0.0002
## 460 0.4825 nan 0.0100 -0.0001
## 480 0.4727 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2483 nan 0.1000 0.0306
## 2 1.1826 nan 0.1000 0.0268
## 3 1.1189 nan 0.1000 0.0292
## 4 1.0711 nan 0.1000 0.0230
## 5 1.0335 nan 0.1000 0.0149
## 6 0.9966 nan 0.1000 0.0168
## 7 0.9630 nan 0.1000 0.0138
## 8 0.9302 nan 0.1000 0.0130
## 9 0.8994 nan 0.1000 0.0111
## 10 0.8727 nan 0.1000 0.0105
## 20 0.7253 nan 0.1000 0.0006
## 40 0.5918 nan 0.1000 -0.0014
## 60 0.5021 nan 0.1000 -0.0016
## 80 0.4391 nan 0.1000 -0.0009
## 100 0.3877 nan 0.1000 -0.0002
## 120 0.3458 nan 0.1000 -0.0008
## 140 0.3072 nan 0.1000 -0.0005
## 160 0.2721 nan 0.1000 -0.0004
## 180 0.2441 nan 0.1000 -0.0001
## 200 0.2215 nan 0.1000 -0.0002
## 220 0.1999 nan 0.1000 0.0001
## 240 0.1821 nan 0.1000 -0.0001
## 260 0.1663 nan 0.1000 -0.0003
## 280 0.1533 nan 0.1000 -0.0005
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## 320 0.1278 nan 0.1000 -0.0004
## 340 0.1169 nan 0.1000 -0.0001
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## 380 0.0985 nan 0.1000 -0.0001
## 400 0.0909 nan 0.1000 0.0000
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## 460 0.0708 nan 0.1000 -0.0001
## 480 0.0654 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2383 nan 0.1000 0.0402
## 2 1.1679 nan 0.1000 0.0306
## 3 1.1096 nan 0.1000 0.0246
## 4 1.0571 nan 0.1000 0.0252
## 5 1.0135 nan 0.1000 0.0163
## 6 0.9796 nan 0.1000 0.0145
## 7 0.9468 nan 0.1000 0.0131
## 8 0.9171 nan 0.1000 0.0122
## 9 0.8901 nan 0.1000 0.0116
## 10 0.8697 nan 0.1000 0.0075
## 20 0.7178 nan 0.1000 0.0022
## 40 0.5903 nan 0.1000 0.0004
## 60 0.5104 nan 0.1000 -0.0007
## 80 0.4429 nan 0.1000 0.0004
## 100 0.3955 nan 0.1000 -0.0011
## 120 0.3543 nan 0.1000 -0.0013
## 140 0.3172 nan 0.1000 0.0004
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## 180 0.2617 nan 0.1000 -0.0004
## 200 0.2372 nan 0.1000 0.0002
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## 240 0.1948 nan 0.1000 -0.0007
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## 320 0.1396 nan 0.1000 -0.0003
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## 380 0.1090 nan 0.1000 -0.0006
## 400 0.1000 nan 0.1000 -0.0002
## 420 0.0930 nan 0.1000 -0.0002
## 440 0.0855 nan 0.1000 -0.0002
## 460 0.0784 nan 0.1000 -0.0002
## 480 0.0731 nan 0.1000 -0.0003
## 500 0.0670 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0386
## 2 1.1708 nan 0.1000 0.0287
## 3 1.1129 nan 0.1000 0.0265
## 4 1.0596 nan 0.1000 0.0223
## 5 1.0126 nan 0.1000 0.0213
## 6 0.9773 nan 0.1000 0.0144
## 7 0.9474 nan 0.1000 0.0112
## 8 0.9179 nan 0.1000 0.0127
## 9 0.8930 nan 0.1000 0.0106
## 10 0.8675 nan 0.1000 0.0087
## 20 0.7225 nan 0.1000 0.0021
## 40 0.5959 nan 0.1000 -0.0010
## 60 0.5162 nan 0.1000 -0.0005
## 80 0.4660 nan 0.1000 -0.0007
## 100 0.4098 nan 0.1000 -0.0012
## 120 0.3703 nan 0.1000 -0.0001
## 140 0.3327 nan 0.1000 -0.0010
## 160 0.3024 nan 0.1000 -0.0004
## 180 0.2746 nan 0.1000 -0.0008
## 200 0.2473 nan 0.1000 -0.0001
## 220 0.2245 nan 0.1000 -0.0006
## 240 0.2046 nan 0.1000 -0.0011
## 260 0.1849 nan 0.1000 -0.0004
## 280 0.1696 nan 0.1000 -0.0006
## 300 0.1545 nan 0.1000 -0.0004
## 320 0.1429 nan 0.1000 -0.0005
## 340 0.1324 nan 0.1000 -0.0006
## 360 0.1224 nan 0.1000 -0.0002
## 380 0.1129 nan 0.1000 -0.0004
## 400 0.1044 nan 0.1000 -0.0004
## 420 0.0976 nan 0.1000 -0.0003
## 440 0.0910 nan 0.1000 -0.0006
## 460 0.0839 nan 0.1000 -0.0003
## 480 0.0783 nan 0.1000 -0.0002
## 500 0.0724 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2361 nan 0.1000 0.0385
## 2 1.1581 nan 0.1000 0.0322
## 3 1.0994 nan 0.1000 0.0239
## 4 1.0521 nan 0.1000 0.0210
## 5 1.0070 nan 0.1000 0.0177
## 6 0.9646 nan 0.1000 0.0179
## 7 0.9290 nan 0.1000 0.0133
## 8 0.8963 nan 0.1000 0.0125
## 9 0.8661 nan 0.1000 0.0111
## 10 0.8413 nan 0.1000 0.0102
## 20 0.6926 nan 0.1000 0.0006
## 40 0.5433 nan 0.1000 0.0003
## 60 0.4517 nan 0.1000 0.0007
## 80 0.3828 nan 0.1000 -0.0006
## 100 0.3256 nan 0.1000 -0.0010
## 120 0.2820 nan 0.1000 -0.0007
## 140 0.2463 nan 0.1000 -0.0003
## 160 0.2157 nan 0.1000 -0.0003
## 180 0.1909 nan 0.1000 -0.0006
## 200 0.1690 nan 0.1000 0.0001
## 220 0.1505 nan 0.1000 -0.0004
## 240 0.1343 nan 0.1000 0.0004
## 260 0.1184 nan 0.1000 -0.0004
## 280 0.1058 nan 0.1000 -0.0003
## 300 0.0957 nan 0.1000 -0.0003
## 320 0.0861 nan 0.1000 -0.0001
## 340 0.0776 nan 0.1000 -0.0001
## 360 0.0702 nan 0.1000 -0.0004
## 380 0.0635 nan 0.1000 0.0000
## 400 0.0576 nan 0.1000 -0.0001
## 420 0.0523 nan 0.1000 -0.0001
## 440 0.0480 nan 0.1000 -0.0002
## 460 0.0435 nan 0.1000 -0.0001
## 480 0.0394 nan 0.1000 -0.0000
## 500 0.0360 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0357
## 2 1.1730 nan 0.1000 0.0261
## 3 1.1129 nan 0.1000 0.0283
## 4 1.0593 nan 0.1000 0.0264
## 5 1.0148 nan 0.1000 0.0194
## 6 0.9754 nan 0.1000 0.0163
## 7 0.9439 nan 0.1000 0.0129
## 8 0.9099 nan 0.1000 0.0139
## 9 0.8850 nan 0.1000 0.0089
## 10 0.8606 nan 0.1000 0.0100
## 20 0.6933 nan 0.1000 0.0024
## 40 0.5384 nan 0.1000 -0.0003
## 60 0.4508 nan 0.1000 -0.0010
## 80 0.3819 nan 0.1000 -0.0000
## 100 0.3260 nan 0.1000 -0.0016
## 120 0.2834 nan 0.1000 -0.0007
## 140 0.2492 nan 0.1000 -0.0014
## 160 0.2154 nan 0.1000 -0.0007
## 180 0.1901 nan 0.1000 -0.0003
## 200 0.1668 nan 0.1000 -0.0004
## 220 0.1491 nan 0.1000 -0.0006
## 240 0.1331 nan 0.1000 -0.0006
## 260 0.1190 nan 0.1000 -0.0001
## 280 0.1059 nan 0.1000 -0.0004
## 300 0.0932 nan 0.1000 -0.0000
## 320 0.0846 nan 0.1000 -0.0006
## 340 0.0757 nan 0.1000 -0.0003
## 360 0.0688 nan 0.1000 -0.0002
## 380 0.0624 nan 0.1000 -0.0001
## 400 0.0559 nan 0.1000 0.0000
## 420 0.0509 nan 0.1000 -0.0003
## 440 0.0456 nan 0.1000 -0.0001
## 460 0.0418 nan 0.1000 -0.0002
## 480 0.0382 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0364
## 2 1.1674 nan 0.1000 0.0301
## 3 1.1078 nan 0.1000 0.0268
## 4 1.0594 nan 0.1000 0.0184
## 5 1.0101 nan 0.1000 0.0223
## 6 0.9692 nan 0.1000 0.0150
## 7 0.9322 nan 0.1000 0.0150
## 8 0.9005 nan 0.1000 0.0105
## 9 0.8757 nan 0.1000 0.0092
## 10 0.8509 nan 0.1000 0.0104
## 20 0.6978 nan 0.1000 0.0037
## 40 0.5521 nan 0.1000 -0.0003
## 60 0.4612 nan 0.1000 0.0006
## 80 0.4010 nan 0.1000 -0.0009
## 100 0.3486 nan 0.1000 -0.0014
## 120 0.3023 nan 0.1000 -0.0005
## 140 0.2687 nan 0.1000 -0.0014
## 160 0.2411 nan 0.1000 -0.0008
## 180 0.2162 nan 0.1000 -0.0004
## 200 0.1938 nan 0.1000 -0.0006
## 220 0.1769 nan 0.1000 -0.0004
## 240 0.1591 nan 0.1000 -0.0009
## 260 0.1424 nan 0.1000 -0.0005
## 280 0.1274 nan 0.1000 -0.0007
## 300 0.1145 nan 0.1000 -0.0005
## 320 0.1045 nan 0.1000 -0.0003
## 340 0.0946 nan 0.1000 -0.0005
## 360 0.0867 nan 0.1000 -0.0005
## 380 0.0780 nan 0.1000 -0.0004
## 400 0.0706 nan 0.1000 -0.0001
## 420 0.0649 nan 0.1000 -0.0003
## 440 0.0590 nan 0.1000 -0.0001
## 460 0.0542 nan 0.1000 -0.0003
## 480 0.0490 nan 0.1000 -0.0002
## 500 0.0442 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0392
## 2 1.1556 nan 0.1000 0.0354
## 3 1.0920 nan 0.1000 0.0266
## 4 1.0376 nan 0.1000 0.0239
## 5 0.9931 nan 0.1000 0.0164
## 6 0.9529 nan 0.1000 0.0104
## 7 0.9194 nan 0.1000 0.0121
## 8 0.8903 nan 0.1000 0.0110
## 9 0.8580 nan 0.1000 0.0143
## 10 0.8321 nan 0.1000 0.0107
## 20 0.6625 nan 0.1000 0.0012
## 40 0.5012 nan 0.1000 -0.0009
## 60 0.4067 nan 0.1000 -0.0002
## 80 0.3337 nan 0.1000 -0.0004
## 100 0.2755 nan 0.1000 -0.0014
## 120 0.2341 nan 0.1000 0.0009
## 140 0.2010 nan 0.1000 -0.0002
## 160 0.1710 nan 0.1000 -0.0005
## 180 0.1482 nan 0.1000 -0.0006
## 200 0.1288 nan 0.1000 -0.0004
## 220 0.1120 nan 0.1000 -0.0005
## 240 0.0982 nan 0.1000 -0.0001
## 260 0.0869 nan 0.1000 -0.0005
## 280 0.0767 nan 0.1000 -0.0004
## 300 0.0684 nan 0.1000 -0.0003
## 320 0.0605 nan 0.1000 -0.0001
## 340 0.0531 nan 0.1000 -0.0001
## 360 0.0470 nan 0.1000 -0.0002
## 380 0.0412 nan 0.1000 -0.0000
## 400 0.0371 nan 0.1000 -0.0001
## 420 0.0326 nan 0.1000 -0.0001
## 440 0.0289 nan 0.1000 -0.0000
## 460 0.0259 nan 0.1000 -0.0000
## 480 0.0233 nan 0.1000 -0.0000
## 500 0.0208 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2288 nan 0.1000 0.0399
## 2 1.1572 nan 0.1000 0.0328
## 3 1.0878 nan 0.1000 0.0307
## 4 1.0359 nan 0.1000 0.0214
## 5 0.9871 nan 0.1000 0.0211
## 6 0.9441 nan 0.1000 0.0177
## 7 0.9103 nan 0.1000 0.0140
## 8 0.8803 nan 0.1000 0.0092
## 9 0.8450 nan 0.1000 0.0125
## 10 0.8216 nan 0.1000 0.0089
## 20 0.6622 nan 0.1000 0.0015
## 40 0.5085 nan 0.1000 0.0006
## 60 0.4171 nan 0.1000 -0.0011
## 80 0.3406 nan 0.1000 -0.0019
## 100 0.2853 nan 0.1000 -0.0006
## 120 0.2429 nan 0.1000 -0.0009
## 140 0.2099 nan 0.1000 -0.0008
## 160 0.1835 nan 0.1000 0.0002
## 180 0.1570 nan 0.1000 0.0000
## 200 0.1355 nan 0.1000 -0.0004
## 220 0.1181 nan 0.1000 -0.0005
## 240 0.1036 nan 0.1000 0.0001
## 260 0.0903 nan 0.1000 -0.0002
## 280 0.0782 nan 0.1000 -0.0003
## 300 0.0689 nan 0.1000 -0.0005
## 320 0.0615 nan 0.1000 -0.0002
## 340 0.0537 nan 0.1000 -0.0002
## 360 0.0475 nan 0.1000 -0.0001
## 380 0.0419 nan 0.1000 -0.0002
## 400 0.0375 nan 0.1000 -0.0001
## 420 0.0331 nan 0.1000 -0.0002
## 440 0.0294 nan 0.1000 -0.0001
## 460 0.0264 nan 0.1000 -0.0001
## 480 0.0232 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2368 nan 0.1000 0.0403
## 2 1.1601 nan 0.1000 0.0285
## 3 1.1013 nan 0.1000 0.0274
## 4 1.0415 nan 0.1000 0.0241
## 5 0.9961 nan 0.1000 0.0159
## 6 0.9587 nan 0.1000 0.0161
## 7 0.9198 nan 0.1000 0.0137
## 8 0.8826 nan 0.1000 0.0143
## 9 0.8552 nan 0.1000 0.0089
## 10 0.8311 nan 0.1000 0.0083
## 20 0.6761 nan 0.1000 0.0034
## 40 0.5186 nan 0.1000 0.0008
## 60 0.4242 nan 0.1000 -0.0013
## 80 0.3561 nan 0.1000 -0.0019
## 100 0.3020 nan 0.1000 -0.0014
## 120 0.2617 nan 0.1000 -0.0012
## 140 0.2262 nan 0.1000 -0.0005
## 160 0.1930 nan 0.1000 -0.0011
## 180 0.1657 nan 0.1000 -0.0007
## 200 0.1460 nan 0.1000 -0.0003
## 220 0.1272 nan 0.1000 -0.0004
## 240 0.1115 nan 0.1000 -0.0008
## 260 0.0983 nan 0.1000 -0.0003
## 280 0.0867 nan 0.1000 -0.0001
## 300 0.0783 nan 0.1000 -0.0005
## 320 0.0691 nan 0.1000 -0.0001
## 340 0.0622 nan 0.1000 -0.0002
## 360 0.0544 nan 0.1000 -0.0001
## 380 0.0482 nan 0.1000 -0.0002
## 400 0.0426 nan 0.1000 -0.0002
## 420 0.0379 nan 0.1000 -0.0001
## 440 0.0336 nan 0.1000 -0.0001
## 460 0.0302 nan 0.1000 -0.0002
## 480 0.0268 nan 0.1000 -0.0002
## 500 0.0241 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0003
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2730 nan 0.0010 0.0004
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2299 nan 0.0010 0.0003
## 140 1.2168 nan 0.0010 0.0003
## 160 1.2041 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0003
## 200 1.1793 nan 0.0010 0.0003
## 220 1.1675 nan 0.0010 0.0002
## 240 1.1561 nan 0.0010 0.0003
## 260 1.1451 nan 0.0010 0.0002
## 280 1.1343 nan 0.0010 0.0002
## 300 1.1239 nan 0.0010 0.0002
## 320 1.1137 nan 0.0010 0.0002
## 340 1.1034 nan 0.0010 0.0002
## 360 1.0939 nan 0.0010 0.0002
## 380 1.0847 nan 0.0010 0.0002
## 400 1.0755 nan 0.0010 0.0002
## 420 1.0665 nan 0.0010 0.0002
## 440 1.0580 nan 0.0010 0.0002
## 460 1.0494 nan 0.0010 0.0002
## 480 1.0413 nan 0.0010 0.0002
## 500 1.0333 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2889 nan 0.0010 0.0004
## 60 1.2737 nan 0.0010 0.0004
## 80 1.2589 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0004
## 120 1.2311 nan 0.0010 0.0003
## 140 1.2179 nan 0.0010 0.0002
## 160 1.2048 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0002
## 200 1.1804 nan 0.0010 0.0003
## 220 1.1688 nan 0.0010 0.0003
## 240 1.1572 nan 0.0010 0.0002
## 260 1.1461 nan 0.0010 0.0002
## 280 1.1356 nan 0.0010 0.0002
## 300 1.1251 nan 0.0010 0.0002
## 320 1.1149 nan 0.0010 0.0002
## 340 1.1047 nan 0.0010 0.0002
## 360 1.0949 nan 0.0010 0.0002
## 380 1.0855 nan 0.0010 0.0002
## 400 1.0763 nan 0.0010 0.0002
## 420 1.0672 nan 0.0010 0.0002
## 440 1.0587 nan 0.0010 0.0002
## 460 1.0503 nan 0.0010 0.0002
## 480 1.0419 nan 0.0010 0.0002
## 500 1.0338 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0003
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2887 nan 0.0010 0.0003
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2590 nan 0.0010 0.0003
## 100 1.2451 nan 0.0010 0.0003
## 120 1.2313 nan 0.0010 0.0003
## 140 1.2179 nan 0.0010 0.0003
## 160 1.2049 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0003
## 200 1.1802 nan 0.0010 0.0003
## 220 1.1683 nan 0.0010 0.0003
## 240 1.1571 nan 0.0010 0.0003
## 260 1.1460 nan 0.0010 0.0003
## 280 1.1353 nan 0.0010 0.0002
## 300 1.1248 nan 0.0010 0.0002
## 320 1.1147 nan 0.0010 0.0002
## 340 1.1048 nan 0.0010 0.0002
## 360 1.0951 nan 0.0010 0.0002
## 380 1.0859 nan 0.0010 0.0002
## 400 1.0768 nan 0.0010 0.0002
## 420 1.0680 nan 0.0010 0.0002
## 440 1.0592 nan 0.0010 0.0002
## 460 1.0510 nan 0.0010 0.0002
## 480 1.0428 nan 0.0010 0.0002
## 500 1.0348 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0003
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0003
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2237 nan 0.0010 0.0004
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1956 nan 0.0010 0.0003
## 180 1.1822 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1446 nan 0.0010 0.0002
## 260 1.1327 nan 0.0010 0.0003
## 280 1.1211 nan 0.0010 0.0002
## 300 1.1096 nan 0.0010 0.0003
## 320 1.0988 nan 0.0010 0.0002
## 340 1.0884 nan 0.0010 0.0002
## 360 1.0781 nan 0.0010 0.0002
## 380 1.0684 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0493 nan 0.0010 0.0002
## 440 1.0400 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0223 nan 0.0010 0.0002
## 500 1.0138 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0003
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0003
## 100 1.2399 nan 0.0010 0.0003
## 120 1.2252 nan 0.0010 0.0003
## 140 1.2112 nan 0.0010 0.0003
## 160 1.1973 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1712 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0003
## 240 1.1465 nan 0.0010 0.0003
## 260 1.1346 nan 0.0010 0.0003
## 280 1.1229 nan 0.0010 0.0003
## 300 1.1116 nan 0.0010 0.0002
## 320 1.1008 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
## 360 1.0797 nan 0.0010 0.0002
## 380 1.0697 nan 0.0010 0.0003
## 400 1.0601 nan 0.0010 0.0002
## 420 1.0506 nan 0.0010 0.0002
## 440 1.0410 nan 0.0010 0.0002
## 460 1.0319 nan 0.0010 0.0002
## 480 1.0235 nan 0.0010 0.0002
## 500 1.0149 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0003
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1982 nan 0.0010 0.0003
## 180 1.1852 nan 0.0010 0.0003
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## 220 1.1600 nan 0.0010 0.0003
## 240 1.1479 nan 0.0010 0.0003
## 260 1.1361 nan 0.0010 0.0003
## 280 1.1247 nan 0.0010 0.0002
## 300 1.1134 nan 0.0010 0.0003
## 320 1.1028 nan 0.0010 0.0003
## 340 1.0928 nan 0.0010 0.0002
## 360 1.0826 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0630 nan 0.0010 0.0002
## 420 1.0537 nan 0.0010 0.0002
## 440 1.0447 nan 0.0010 0.0002
## 460 1.0358 nan 0.0010 0.0002
## 480 1.0271 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0004
## 100 1.2365 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2057 nan 0.0010 0.0003
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## 180 1.1770 nan 0.0010 0.0003
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## 340 1.0788 nan 0.0010 0.0002
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## 380 1.0575 nan 0.0010 0.0002
## 400 1.0474 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
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## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0005
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2681 nan 0.0010 0.0004
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## 100 1.2364 nan 0.0010 0.0003
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## 160 1.1917 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1647 nan 0.0010 0.0003
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1390 nan 0.0010 0.0002
## 260 1.1266 nan 0.0010 0.0003
## 280 1.1147 nan 0.0010 0.0003
## 300 1.1030 nan 0.0010 0.0002
## 320 1.0915 nan 0.0010 0.0003
## 340 1.0804 nan 0.0010 0.0002
## 360 1.0699 nan 0.0010 0.0002
## 380 1.0593 nan 0.0010 0.0003
## 400 1.0491 nan 0.0010 0.0002
## 420 1.0395 nan 0.0010 0.0002
## 440 1.0299 nan 0.0010 0.0002
## 460 1.0207 nan 0.0010 0.0002
## 480 1.0115 nan 0.0010 0.0002
## 500 1.0025 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0003
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0003
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2367 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2070 nan 0.0010 0.0003
## 160 1.1929 nan 0.0010 0.0003
## 180 1.1793 nan 0.0010 0.0003
## 200 1.1661 nan 0.0010 0.0003
## 220 1.1534 nan 0.0010 0.0002
## 240 1.1408 nan 0.0010 0.0003
## 260 1.1287 nan 0.0010 0.0003
## 280 1.1166 nan 0.0010 0.0003
## 300 1.1051 nan 0.0010 0.0002
## 320 1.0942 nan 0.0010 0.0002
## 340 1.0837 nan 0.0010 0.0002
## 360 1.0731 nan 0.0010 0.0002
## 380 1.0628 nan 0.0010 0.0002
## 400 1.0530 nan 0.0010 0.0002
## 420 1.0433 nan 0.0010 0.0002
## 440 1.0337 nan 0.0010 0.0002
## 460 1.0245 nan 0.0010 0.0002
## 480 1.0156 nan 0.0010 0.0002
## 500 1.0068 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3029 nan 0.0100 0.0041
## 3 1.2953 nan 0.0100 0.0036
## 4 1.2883 nan 0.0100 0.0033
## 5 1.2803 nan 0.0100 0.0036
## 6 1.2729 nan 0.0100 0.0029
## 7 1.2657 nan 0.0100 0.0033
## 8 1.2583 nan 0.0100 0.0033
## 9 1.2512 nan 0.0100 0.0029
## 10 1.2440 nan 0.0100 0.0029
## 20 1.1785 nan 0.0100 0.0028
## 40 1.0757 nan 0.0100 0.0020
## 60 0.9971 nan 0.0100 0.0015
## 80 0.9334 nan 0.0100 0.0010
## 100 0.8817 nan 0.0100 0.0007
## 120 0.8408 nan 0.0100 0.0008
## 140 0.8063 nan 0.0100 0.0006
## 160 0.7765 nan 0.0100 0.0004
## 180 0.7520 nan 0.0100 0.0004
## 200 0.7292 nan 0.0100 0.0002
## 220 0.7093 nan 0.0100 0.0000
## 240 0.6929 nan 0.0100 -0.0000
## 260 0.6775 nan 0.0100 0.0000
## 280 0.6639 nan 0.0100 0.0001
## 300 0.6509 nan 0.0100 0.0000
## 320 0.6379 nan 0.0100 -0.0000
## 340 0.6261 nan 0.0100 -0.0001
## 360 0.6149 nan 0.0100 0.0001
## 380 0.6038 nan 0.0100 -0.0001
## 400 0.5937 nan 0.0100 -0.0000
## 420 0.5840 nan 0.0100 -0.0000
## 440 0.5742 nan 0.0100 -0.0000
## 460 0.5654 nan 0.0100 0.0000
## 480 0.5557 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0042
## 2 1.3034 nan 0.0100 0.0036
## 3 1.2958 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0032
## 5 1.2804 nan 0.0100 0.0037
## 6 1.2735 nan 0.0100 0.0030
## 7 1.2665 nan 0.0100 0.0031
## 8 1.2592 nan 0.0100 0.0034
## 9 1.2522 nan 0.0100 0.0034
## 10 1.2449 nan 0.0100 0.0033
## 20 1.1796 nan 0.0100 0.0024
## 40 1.0769 nan 0.0100 0.0017
## 60 0.9979 nan 0.0100 0.0016
## 80 0.9347 nan 0.0100 0.0012
## 100 0.8849 nan 0.0100 0.0008
## 120 0.8436 nan 0.0100 0.0007
## 140 0.8105 nan 0.0100 0.0005
## 160 0.7808 nan 0.0100 0.0001
## 180 0.7553 nan 0.0100 0.0003
## 200 0.7333 nan 0.0100 0.0003
## 220 0.7139 nan 0.0100 0.0003
## 240 0.6972 nan 0.0100 0.0001
## 260 0.6817 nan 0.0100 0.0003
## 280 0.6681 nan 0.0100 -0.0001
## 300 0.6545 nan 0.0100 0.0000
## 320 0.6425 nan 0.0100 0.0000
## 340 0.6318 nan 0.0100 0.0000
## 360 0.6203 nan 0.0100 -0.0001
## 380 0.6090 nan 0.0100 -0.0000
## 400 0.5992 nan 0.0100 0.0001
## 420 0.5893 nan 0.0100 0.0000
## 440 0.5805 nan 0.0100 -0.0001
## 460 0.5713 nan 0.0100 -0.0001
## 480 0.5634 nan 0.0100 -0.0001
## 500 0.5548 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3040 nan 0.0100 0.0039
## 3 1.2957 nan 0.0100 0.0039
## 4 1.2873 nan 0.0100 0.0032
## 5 1.2798 nan 0.0100 0.0035
## 6 1.2724 nan 0.0100 0.0032
## 7 1.2656 nan 0.0100 0.0034
## 8 1.2587 nan 0.0100 0.0034
## 9 1.2513 nan 0.0100 0.0031
## 10 1.2444 nan 0.0100 0.0030
## 20 1.1806 nan 0.0100 0.0027
## 40 1.0775 nan 0.0100 0.0020
## 60 0.9982 nan 0.0100 0.0013
## 80 0.9356 nan 0.0100 0.0012
## 100 0.8841 nan 0.0100 0.0010
## 120 0.8436 nan 0.0100 0.0007
## 140 0.8077 nan 0.0100 0.0004
## 160 0.7789 nan 0.0100 0.0003
## 180 0.7541 nan 0.0100 0.0003
## 200 0.7333 nan 0.0100 0.0002
## 220 0.7156 nan 0.0100 0.0000
## 240 0.6992 nan 0.0100 0.0001
## 260 0.6832 nan 0.0100 0.0003
## 280 0.6699 nan 0.0100 0.0001
## 300 0.6569 nan 0.0100 0.0000
## 320 0.6450 nan 0.0100 0.0002
## 340 0.6341 nan 0.0100 -0.0002
## 360 0.6224 nan 0.0100 -0.0000
## 380 0.6113 nan 0.0100 -0.0001
## 400 0.6020 nan 0.0100 0.0001
## 420 0.5932 nan 0.0100 0.0000
## 440 0.5839 nan 0.0100 -0.0001
## 460 0.5749 nan 0.0100 -0.0001
## 480 0.5663 nan 0.0100 -0.0002
## 500 0.5575 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3028 nan 0.0100 0.0036
## 3 1.2942 nan 0.0100 0.0038
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2776 nan 0.0100 0.0034
## 6 1.2698 nan 0.0100 0.0036
## 7 1.2629 nan 0.0100 0.0033
## 8 1.2551 nan 0.0100 0.0035
## 9 1.2477 nan 0.0100 0.0033
## 10 1.2405 nan 0.0100 0.0032
## 20 1.1697 nan 0.0100 0.0030
## 40 1.0612 nan 0.0100 0.0018
## 60 0.9762 nan 0.0100 0.0014
## 80 0.9086 nan 0.0100 0.0011
## 100 0.8536 nan 0.0100 0.0010
## 120 0.8079 nan 0.0100 0.0006
## 140 0.7707 nan 0.0100 0.0004
## 160 0.7392 nan 0.0100 0.0002
## 180 0.7122 nan 0.0100 0.0002
## 200 0.6895 nan 0.0100 0.0003
## 220 0.6680 nan 0.0100 0.0000
## 240 0.6484 nan 0.0100 -0.0001
## 260 0.6307 nan 0.0100 0.0001
## 280 0.6157 nan 0.0100 0.0000
## 300 0.6010 nan 0.0100 0.0002
## 320 0.5873 nan 0.0100 0.0001
## 340 0.5741 nan 0.0100 0.0000
## 360 0.5608 nan 0.0100 -0.0002
## 380 0.5494 nan 0.0100 -0.0000
## 400 0.5374 nan 0.0100 -0.0000
## 420 0.5269 nan 0.0100 -0.0001
## 440 0.5161 nan 0.0100 0.0000
## 460 0.5056 nan 0.0100 0.0000
## 480 0.4956 nan 0.0100 -0.0000
## 500 0.4873 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3033 nan 0.0100 0.0038
## 3 1.2948 nan 0.0100 0.0038
## 4 1.2863 nan 0.0100 0.0037
## 5 1.2779 nan 0.0100 0.0038
## 6 1.2704 nan 0.0100 0.0034
## 7 1.2633 nan 0.0100 0.0032
## 8 1.2554 nan 0.0100 0.0035
## 9 1.2481 nan 0.0100 0.0033
## 10 1.2404 nan 0.0100 0.0032
## 20 1.1724 nan 0.0100 0.0029
## 40 1.0628 nan 0.0100 0.0019
## 60 0.9781 nan 0.0100 0.0014
## 80 0.9117 nan 0.0100 0.0011
## 100 0.8587 nan 0.0100 0.0007
## 120 0.8146 nan 0.0100 0.0008
## 140 0.7778 nan 0.0100 0.0006
## 160 0.7463 nan 0.0100 0.0004
## 180 0.7194 nan 0.0100 0.0004
## 200 0.6968 nan 0.0100 0.0002
## 220 0.6768 nan 0.0100 0.0002
## 240 0.6583 nan 0.0100 0.0001
## 260 0.6403 nan 0.0100 0.0001
## 280 0.6232 nan 0.0100 0.0003
## 300 0.6086 nan 0.0100 -0.0000
## 320 0.5963 nan 0.0100 0.0000
## 340 0.5832 nan 0.0100 0.0002
## 360 0.5714 nan 0.0100 -0.0001
## 380 0.5599 nan 0.0100 -0.0002
## 400 0.5482 nan 0.0100 -0.0001
## 420 0.5379 nan 0.0100 -0.0002
## 440 0.5275 nan 0.0100 0.0000
## 460 0.5172 nan 0.0100 -0.0001
## 480 0.5078 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3028 nan 0.0100 0.0040
## 3 1.2942 nan 0.0100 0.0037
## 4 1.2859 nan 0.0100 0.0034
## 5 1.2779 nan 0.0100 0.0035
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2630 nan 0.0100 0.0031
## 8 1.2552 nan 0.0100 0.0032
## 9 1.2483 nan 0.0100 0.0029
## 10 1.2406 nan 0.0100 0.0033
## 20 1.1722 nan 0.0100 0.0027
## 40 1.0626 nan 0.0100 0.0020
## 60 0.9787 nan 0.0100 0.0017
## 80 0.9126 nan 0.0100 0.0013
## 100 0.8621 nan 0.0100 0.0007
## 120 0.8183 nan 0.0100 0.0006
## 140 0.7826 nan 0.0100 0.0007
## 160 0.7523 nan 0.0100 0.0003
## 180 0.7268 nan 0.0100 0.0003
## 200 0.7035 nan 0.0100 0.0002
## 220 0.6835 nan 0.0100 0.0000
## 240 0.6646 nan 0.0100 0.0002
## 260 0.6475 nan 0.0100 0.0000
## 280 0.6319 nan 0.0100 -0.0001
## 300 0.6174 nan 0.0100 -0.0001
## 320 0.6048 nan 0.0100 -0.0001
## 340 0.5929 nan 0.0100 -0.0001
## 360 0.5812 nan 0.0100 0.0003
## 380 0.5705 nan 0.0100 0.0000
## 400 0.5599 nan 0.0100 -0.0000
## 420 0.5492 nan 0.0100 0.0001
## 440 0.5383 nan 0.0100 0.0000
## 460 0.5295 nan 0.0100 0.0001
## 480 0.5202 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2932 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0040
## 5 1.2759 nan 0.0100 0.0038
## 6 1.2673 nan 0.0100 0.0037
## 7 1.2586 nan 0.0100 0.0038
## 8 1.2501 nan 0.0100 0.0036
## 9 1.2418 nan 0.0100 0.0036
## 10 1.2341 nan 0.0100 0.0037
## 20 1.1612 nan 0.0100 0.0031
## 40 1.0443 nan 0.0100 0.0023
## 60 0.9556 nan 0.0100 0.0017
## 80 0.8873 nan 0.0100 0.0010
## 100 0.8324 nan 0.0100 0.0011
## 120 0.7860 nan 0.0100 0.0007
## 140 0.7480 nan 0.0100 0.0005
## 160 0.7145 nan 0.0100 0.0004
## 180 0.6846 nan 0.0100 0.0002
## 200 0.6589 nan 0.0100 0.0003
## 220 0.6351 nan 0.0100 0.0003
## 240 0.6151 nan 0.0100 0.0002
## 260 0.5969 nan 0.0100 0.0001
## 280 0.5795 nan 0.0100 0.0000
## 300 0.5631 nan 0.0100 0.0001
## 320 0.5477 nan 0.0100 -0.0000
## 340 0.5329 nan 0.0100 0.0000
## 360 0.5191 nan 0.0100 -0.0000
## 380 0.5071 nan 0.0100 0.0000
## 400 0.4956 nan 0.0100 0.0001
## 420 0.4847 nan 0.0100 -0.0001
## 440 0.4732 nan 0.0100 -0.0001
## 460 0.4626 nan 0.0100 0.0000
## 480 0.4518 nan 0.0100 -0.0002
## 500 0.4422 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2929 nan 0.0100 0.0043
## 4 1.2841 nan 0.0100 0.0034
## 5 1.2757 nan 0.0100 0.0038
## 6 1.2676 nan 0.0100 0.0038
## 7 1.2597 nan 0.0100 0.0033
## 8 1.2514 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0035
## 10 1.2356 nan 0.0100 0.0034
## 20 1.1646 nan 0.0100 0.0030
## 40 1.0480 nan 0.0100 0.0021
## 60 0.9604 nan 0.0100 0.0015
## 80 0.8912 nan 0.0100 0.0012
## 100 0.8362 nan 0.0100 0.0008
## 120 0.7907 nan 0.0100 0.0005
## 140 0.7519 nan 0.0100 0.0006
## 160 0.7191 nan 0.0100 0.0004
## 180 0.6922 nan 0.0100 0.0001
## 200 0.6664 nan 0.0100 -0.0001
## 220 0.6443 nan 0.0100 0.0001
## 240 0.6235 nan 0.0100 0.0001
## 260 0.6051 nan 0.0100 0.0001
## 280 0.5879 nan 0.0100 0.0000
## 300 0.5714 nan 0.0100 -0.0000
## 320 0.5562 nan 0.0100 -0.0001
## 340 0.5425 nan 0.0100 -0.0001
## 360 0.5298 nan 0.0100 0.0000
## 380 0.5166 nan 0.0100 -0.0000
## 400 0.5042 nan 0.0100 -0.0001
## 420 0.4932 nan 0.0100 0.0000
## 440 0.4828 nan 0.0100 0.0001
## 460 0.4731 nan 0.0100 -0.0000
## 480 0.4624 nan 0.0100 -0.0001
## 500 0.4519 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0039
## 4 1.2839 nan 0.0100 0.0039
## 5 1.2757 nan 0.0100 0.0040
## 6 1.2678 nan 0.0100 0.0034
## 7 1.2599 nan 0.0100 0.0039
## 8 1.2523 nan 0.0100 0.0032
## 9 1.2448 nan 0.0100 0.0034
## 10 1.2370 nan 0.0100 0.0040
## 20 1.1685 nan 0.0100 0.0027
## 40 1.0564 nan 0.0100 0.0023
## 60 0.9678 nan 0.0100 0.0015
## 80 0.8995 nan 0.0100 0.0012
## 100 0.8458 nan 0.0100 0.0012
## 120 0.8007 nan 0.0100 0.0008
## 140 0.7640 nan 0.0100 0.0005
## 160 0.7310 nan 0.0100 0.0004
## 180 0.7031 nan 0.0100 0.0003
## 200 0.6805 nan 0.0100 0.0002
## 220 0.6585 nan 0.0100 0.0001
## 240 0.6390 nan 0.0100 0.0001
## 260 0.6213 nan 0.0100 0.0001
## 280 0.6047 nan 0.0100 0.0001
## 300 0.5876 nan 0.0100 0.0001
## 320 0.5731 nan 0.0100 0.0000
## 340 0.5600 nan 0.0100 -0.0000
## 360 0.5466 nan 0.0100 -0.0001
## 380 0.5336 nan 0.0100 0.0002
## 400 0.5220 nan 0.0100 -0.0000
## 420 0.5110 nan 0.0100 -0.0001
## 440 0.4994 nan 0.0100 -0.0001
## 460 0.4880 nan 0.0100 0.0000
## 480 0.4783 nan 0.0100 -0.0001
## 500 0.4686 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0361
## 2 1.1722 nan 0.1000 0.0271
## 3 1.1241 nan 0.1000 0.0208
## 4 1.0768 nan 0.1000 0.0178
## 5 1.0345 nan 0.1000 0.0170
## 6 0.9970 nan 0.1000 0.0151
## 7 0.9635 nan 0.1000 0.0134
## 8 0.9322 nan 0.1000 0.0127
## 9 0.9038 nan 0.1000 0.0103
## 10 0.8834 nan 0.1000 0.0058
## 20 0.7239 nan 0.1000 0.0003
## 40 0.5859 nan 0.1000 0.0006
## 60 0.5066 nan 0.1000 -0.0014
## 80 0.4385 nan 0.1000 -0.0016
## 100 0.3846 nan 0.1000 -0.0004
## 120 0.3490 nan 0.1000 -0.0008
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2814 nan 0.1000 -0.0001
## 180 0.2532 nan 0.1000 -0.0006
## 200 0.2285 nan 0.1000 0.0002
## 220 0.2057 nan 0.1000 -0.0007
## 240 0.1845 nan 0.1000 -0.0004
## 260 0.1663 nan 0.1000 -0.0005
## 280 0.1522 nan 0.1000 -0.0001
## 300 0.1384 nan 0.1000 -0.0004
## 320 0.1258 nan 0.1000 -0.0001
## 340 0.1145 nan 0.1000 0.0001
## 360 0.1043 nan 0.1000 0.0001
## 380 0.0962 nan 0.1000 -0.0001
## 400 0.0882 nan 0.1000 -0.0000
## 420 0.0813 nan 0.1000 -0.0003
## 440 0.0752 nan 0.1000 -0.0001
## 460 0.0695 nan 0.1000 -0.0002
## 480 0.0639 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0368
## 2 1.1800 nan 0.1000 0.0257
## 3 1.1249 nan 0.1000 0.0253
## 4 1.0707 nan 0.1000 0.0227
## 5 1.0281 nan 0.1000 0.0169
## 6 0.9956 nan 0.1000 0.0148
## 7 0.9642 nan 0.1000 0.0109
## 8 0.9372 nan 0.1000 0.0108
## 9 0.9085 nan 0.1000 0.0116
## 10 0.8823 nan 0.1000 0.0095
## 20 0.7362 nan 0.1000 0.0015
## 40 0.6135 nan 0.1000 -0.0018
## 60 0.5279 nan 0.1000 -0.0005
## 80 0.4641 nan 0.1000 0.0003
## 100 0.4138 nan 0.1000 -0.0011
## 120 0.3682 nan 0.1000 -0.0022
## 140 0.3332 nan 0.1000 -0.0009
## 160 0.2992 nan 0.1000 -0.0010
## 180 0.2715 nan 0.1000 -0.0006
## 200 0.2462 nan 0.1000 -0.0003
## 220 0.2246 nan 0.1000 -0.0002
## 240 0.2051 nan 0.1000 -0.0006
## 260 0.1863 nan 0.1000 -0.0003
## 280 0.1709 nan 0.1000 -0.0005
## 300 0.1551 nan 0.1000 -0.0004
## 320 0.1436 nan 0.1000 -0.0006
## 340 0.1327 nan 0.1000 -0.0003
## 360 0.1214 nan 0.1000 -0.0006
## 380 0.1117 nan 0.1000 -0.0000
## 400 0.1024 nan 0.1000 -0.0003
## 420 0.0961 nan 0.1000 -0.0001
## 440 0.0881 nan 0.1000 -0.0001
## 460 0.0819 nan 0.1000 -0.0004
## 480 0.0760 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2413 nan 0.1000 0.0360
## 2 1.1728 nan 0.1000 0.0297
## 3 1.1214 nan 0.1000 0.0216
## 4 1.0726 nan 0.1000 0.0212
## 5 1.0274 nan 0.1000 0.0192
## 6 0.9897 nan 0.1000 0.0161
## 7 0.9557 nan 0.1000 0.0147
## 8 0.9329 nan 0.1000 0.0087
## 9 0.9073 nan 0.1000 0.0091
## 10 0.8776 nan 0.1000 0.0128
## 20 0.7336 nan 0.1000 0.0023
## 40 0.6027 nan 0.1000 0.0001
## 60 0.5247 nan 0.1000 -0.0004
## 80 0.4603 nan 0.1000 0.0002
## 100 0.4095 nan 0.1000 0.0000
## 120 0.3634 nan 0.1000 0.0003
## 140 0.3272 nan 0.1000 -0.0009
## 160 0.2945 nan 0.1000 -0.0005
## 180 0.2709 nan 0.1000 -0.0009
## 200 0.2481 nan 0.1000 -0.0005
## 220 0.2256 nan 0.1000 -0.0006
## 240 0.2067 nan 0.1000 -0.0009
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## 380 0.1189 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2407 nan 0.1000 0.0394
## 2 1.1664 nan 0.1000 0.0345
## 3 1.1055 nan 0.1000 0.0273
## 4 1.0577 nan 0.1000 0.0185
## 5 1.0167 nan 0.1000 0.0171
## 6 0.9801 nan 0.1000 0.0154
## 7 0.9461 nan 0.1000 0.0127
## 8 0.9154 nan 0.1000 0.0111
## 9 0.8882 nan 0.1000 0.0113
## 10 0.8582 nan 0.1000 0.0114
## 20 0.6922 nan 0.1000 0.0017
## 40 0.5419 nan 0.1000 0.0004
## 60 0.4596 nan 0.1000 -0.0013
## 80 0.3912 nan 0.1000 0.0003
## 100 0.3396 nan 0.1000 -0.0004
## 120 0.2887 nan 0.1000 -0.0007
## 140 0.2524 nan 0.1000 -0.0001
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## 180 0.1915 nan 0.1000 0.0000
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## 220 0.1515 nan 0.1000 0.0001
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## 280 0.1069 nan 0.1000 -0.0003
## 300 0.0957 nan 0.1000 -0.0002
## 320 0.0838 nan 0.1000 0.0000
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## 380 0.0622 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0357
## 2 1.1712 nan 0.1000 0.0289
## 3 1.1099 nan 0.1000 0.0272
## 4 1.0565 nan 0.1000 0.0207
## 5 1.0106 nan 0.1000 0.0189
## 6 0.9661 nan 0.1000 0.0191
## 7 0.9332 nan 0.1000 0.0117
## 8 0.9000 nan 0.1000 0.0116
## 9 0.8742 nan 0.1000 0.0095
## 10 0.8471 nan 0.1000 0.0103
## 20 0.6991 nan 0.1000 0.0026
## 40 0.5613 nan 0.1000 -0.0014
## 60 0.4725 nan 0.1000 -0.0013
## 80 0.4056 nan 0.1000 0.0002
## 100 0.3510 nan 0.1000 -0.0005
## 120 0.3024 nan 0.1000 -0.0010
## 140 0.2663 nan 0.1000 -0.0007
## 160 0.2359 nan 0.1000 -0.0007
## 180 0.2099 nan 0.1000 -0.0009
## 200 0.1861 nan 0.1000 -0.0005
## 220 0.1633 nan 0.1000 0.0001
## 240 0.1462 nan 0.1000 -0.0003
## 260 0.1301 nan 0.1000 -0.0004
## 280 0.1162 nan 0.1000 -0.0007
## 300 0.1056 nan 0.1000 -0.0004
## 320 0.0952 nan 0.1000 -0.0003
## 340 0.0859 nan 0.1000 -0.0005
## 360 0.0771 nan 0.1000 -0.0003
## 380 0.0694 nan 0.1000 -0.0002
## 400 0.0619 nan 0.1000 -0.0001
## 420 0.0557 nan 0.1000 -0.0002
## 440 0.0505 nan 0.1000 -0.0002
## 460 0.0457 nan 0.1000 -0.0002
## 480 0.0410 nan 0.1000 -0.0000
## 500 0.0370 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2387 nan 0.1000 0.0383
## 2 1.1700 nan 0.1000 0.0316
## 3 1.1112 nan 0.1000 0.0259
## 4 1.0615 nan 0.1000 0.0199
## 5 1.0132 nan 0.1000 0.0221
## 6 0.9741 nan 0.1000 0.0170
## 7 0.9389 nan 0.1000 0.0150
## 8 0.9125 nan 0.1000 0.0084
## 9 0.8851 nan 0.1000 0.0100
## 10 0.8644 nan 0.1000 0.0048
## 20 0.7091 nan 0.1000 0.0011
## 40 0.5621 nan 0.1000 -0.0020
## 60 0.4661 nan 0.1000 0.0008
## 80 0.3954 nan 0.1000 0.0000
## 100 0.3429 nan 0.1000 -0.0011
## 120 0.2972 nan 0.1000 -0.0009
## 140 0.2613 nan 0.1000 -0.0020
## 160 0.2318 nan 0.1000 -0.0005
## 180 0.2075 nan 0.1000 -0.0008
## 200 0.1850 nan 0.1000 -0.0008
## 220 0.1646 nan 0.1000 -0.0005
## 240 0.1495 nan 0.1000 -0.0010
## 260 0.1349 nan 0.1000 -0.0004
## 280 0.1215 nan 0.1000 -0.0004
## 300 0.1097 nan 0.1000 -0.0006
## 320 0.0981 nan 0.1000 -0.0003
## 340 0.0883 nan 0.1000 -0.0002
## 360 0.0795 nan 0.1000 -0.0002
## 380 0.0728 nan 0.1000 -0.0005
## 400 0.0657 nan 0.1000 -0.0001
## 420 0.0597 nan 0.1000 -0.0002
## 440 0.0540 nan 0.1000 -0.0001
## 460 0.0492 nan 0.1000 -0.0002
## 480 0.0444 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2343 nan 0.1000 0.0362
## 2 1.1576 nan 0.1000 0.0301
## 3 1.0929 nan 0.1000 0.0277
## 4 1.0390 nan 0.1000 0.0227
## 5 0.9953 nan 0.1000 0.0196
## 6 0.9556 nan 0.1000 0.0172
## 7 0.9171 nan 0.1000 0.0139
## 8 0.8886 nan 0.1000 0.0111
## 9 0.8605 nan 0.1000 0.0094
## 10 0.8324 nan 0.1000 0.0106
## 20 0.6656 nan 0.1000 0.0025
## 40 0.5056 nan 0.1000 -0.0002
## 60 0.4084 nan 0.1000 -0.0001
## 80 0.3370 nan 0.1000 -0.0004
## 100 0.2806 nan 0.1000 -0.0001
## 120 0.2350 nan 0.1000 -0.0003
## 140 0.1994 nan 0.1000 -0.0005
## 160 0.1704 nan 0.1000 -0.0000
## 180 0.1460 nan 0.1000 0.0002
## 200 0.1282 nan 0.1000 -0.0004
## 220 0.1110 nan 0.1000 -0.0002
## 240 0.0973 nan 0.1000 -0.0003
## 260 0.0847 nan 0.1000 -0.0003
## 280 0.0741 nan 0.1000 -0.0001
## 300 0.0637 nan 0.1000 -0.0000
## 320 0.0559 nan 0.1000 -0.0001
## 340 0.0486 nan 0.1000 -0.0000
## 360 0.0433 nan 0.1000 -0.0002
## 380 0.0382 nan 0.1000 -0.0001
## 400 0.0339 nan 0.1000 -0.0001
## 420 0.0299 nan 0.1000 -0.0001
## 440 0.0269 nan 0.1000 -0.0000
## 460 0.0241 nan 0.1000 -0.0001
## 480 0.0215 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0412
## 2 1.1610 nan 0.1000 0.0307
## 3 1.0957 nan 0.1000 0.0271
## 4 1.0462 nan 0.1000 0.0202
## 5 0.9971 nan 0.1000 0.0196
## 6 0.9606 nan 0.1000 0.0137
## 7 0.9233 nan 0.1000 0.0135
## 8 0.8942 nan 0.1000 0.0111
## 9 0.8654 nan 0.1000 0.0119
## 10 0.8413 nan 0.1000 0.0083
## 20 0.6708 nan 0.1000 0.0020
## 40 0.5172 nan 0.1000 -0.0002
## 60 0.4258 nan 0.1000 -0.0000
## 80 0.3509 nan 0.1000 -0.0003
## 100 0.2907 nan 0.1000 -0.0002
## 120 0.2433 nan 0.1000 -0.0013
## 140 0.2096 nan 0.1000 -0.0006
## 160 0.1790 nan 0.1000 -0.0012
## 180 0.1568 nan 0.1000 -0.0005
## 200 0.1376 nan 0.1000 -0.0001
## 220 0.1209 nan 0.1000 -0.0004
## 240 0.1059 nan 0.1000 -0.0004
## 260 0.0917 nan 0.1000 -0.0002
## 280 0.0799 nan 0.1000 -0.0001
## 300 0.0705 nan 0.1000 -0.0003
## 320 0.0613 nan 0.1000 -0.0003
## 340 0.0538 nan 0.1000 -0.0001
## 360 0.0478 nan 0.1000 -0.0001
## 380 0.0425 nan 0.1000 -0.0002
## 400 0.0379 nan 0.1000 -0.0001
## 420 0.0331 nan 0.1000 -0.0001
## 440 0.0292 nan 0.1000 -0.0001
## 460 0.0266 nan 0.1000 -0.0001
## 480 0.0235 nan 0.1000 -0.0001
## 500 0.0209 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0381
## 2 1.1645 nan 0.1000 0.0325
## 3 1.1032 nan 0.1000 0.0220
## 4 1.0445 nan 0.1000 0.0248
## 5 1.0016 nan 0.1000 0.0186
## 6 0.9501 nan 0.1000 0.0209
## 7 0.9195 nan 0.1000 0.0121
## 8 0.8871 nan 0.1000 0.0109
## 9 0.8610 nan 0.1000 0.0103
## 10 0.8342 nan 0.1000 0.0102
## 20 0.6795 nan 0.1000 0.0027
## 40 0.5304 nan 0.1000 -0.0004
## 60 0.4416 nan 0.1000 -0.0012
## 80 0.3697 nan 0.1000 -0.0010
## 100 0.3057 nan 0.1000 -0.0005
## 120 0.2625 nan 0.1000 -0.0008
## 140 0.2282 nan 0.1000 -0.0007
## 160 0.1951 nan 0.1000 -0.0003
## 180 0.1697 nan 0.1000 -0.0004
## 200 0.1495 nan 0.1000 -0.0003
## 220 0.1312 nan 0.1000 -0.0007
## 240 0.1148 nan 0.1000 -0.0006
## 260 0.0999 nan 0.1000 -0.0006
## 280 0.0883 nan 0.1000 -0.0004
## 300 0.0789 nan 0.1000 -0.0003
## 320 0.0693 nan 0.1000 -0.0005
## 340 0.0612 nan 0.1000 -0.0002
## 360 0.0541 nan 0.1000 -0.0001
## 380 0.0484 nan 0.1000 -0.0003
## 400 0.0433 nan 0.1000 -0.0002
## 420 0.0382 nan 0.1000 -0.0001
## 440 0.0337 nan 0.1000 -0.0000
## 460 0.0304 nan 0.1000 -0.0001
## 480 0.0272 nan 0.1000 -0.0002
## 500 0.0241 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0004
## 100 1.2396 nan 0.0010 0.0003
## 120 1.2251 nan 0.0010 0.0003
## 140 1.2113 nan 0.0010 0.0003
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1719 nan 0.0010 0.0003
## 220 1.1595 nan 0.0010 0.0002
## 240 1.1476 nan 0.0010 0.0003
## 260 1.1360 nan 0.0010 0.0003
## 280 1.1243 nan 0.0010 0.0003
## 300 1.1133 nan 0.0010 0.0002
## 320 1.1026 nan 0.0010 0.0002
## 340 1.0921 nan 0.0010 0.0002
## 360 1.0818 nan 0.0010 0.0002
## 380 1.0721 nan 0.0010 0.0002
## 400 1.0625 nan 0.0010 0.0002
## 420 1.0531 nan 0.0010 0.0002
## 440 1.0439 nan 0.0010 0.0002
## 460 1.0350 nan 0.0010 0.0002
## 480 1.0265 nan 0.0010 0.0002
## 500 1.0183 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0003
## 60 1.2712 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0004
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2262 nan 0.0010 0.0003
## 140 1.2123 nan 0.0010 0.0003
## 160 1.1989 nan 0.0010 0.0003
## 180 1.1856 nan 0.0010 0.0003
## 200 1.1727 nan 0.0010 0.0003
## 220 1.1602 nan 0.0010 0.0003
## 240 1.1480 nan 0.0010 0.0003
## 260 1.1363 nan 0.0010 0.0003
## 280 1.1247 nan 0.0010 0.0003
## 300 1.1137 nan 0.0010 0.0002
## 320 1.1028 nan 0.0010 0.0003
## 340 1.0926 nan 0.0010 0.0002
## 360 1.0824 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0534 nan 0.0010 0.0002
## 440 1.0443 nan 0.0010 0.0002
## 460 1.0353 nan 0.0010 0.0002
## 480 1.0269 nan 0.0010 0.0002
## 500 1.0183 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0003
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2422 nan 0.0010 0.0003
## 120 1.2279 nan 0.0010 0.0003
## 140 1.2138 nan 0.0010 0.0003
## 160 1.2007 nan 0.0010 0.0003
## 180 1.1876 nan 0.0010 0.0003
## 200 1.1749 nan 0.0010 0.0002
## 220 1.1625 nan 0.0010 0.0002
## 240 1.1508 nan 0.0010 0.0003
## 260 1.1394 nan 0.0010 0.0003
## 280 1.1283 nan 0.0010 0.0002
## 300 1.1176 nan 0.0010 0.0002
## 320 1.1067 nan 0.0010 0.0002
## 340 1.0965 nan 0.0010 0.0002
## 360 1.0863 nan 0.0010 0.0002
## 380 1.0766 nan 0.0010 0.0002
## 400 1.0669 nan 0.0010 0.0002
## 420 1.0576 nan 0.0010 0.0002
## 440 1.0486 nan 0.0010 0.0002
## 460 1.0398 nan 0.0010 0.0002
## 480 1.0311 nan 0.0010 0.0002
## 500 1.0228 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0005
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2348 nan 0.0010 0.0004
## 120 1.2191 nan 0.0010 0.0003
## 140 1.2041 nan 0.0010 0.0004
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1754 nan 0.0010 0.0003
## 200 1.1620 nan 0.0010 0.0003
## 220 1.1492 nan 0.0010 0.0003
## 240 1.1366 nan 0.0010 0.0003
## 260 1.1243 nan 0.0010 0.0003
## 280 1.1123 nan 0.0010 0.0003
## 300 1.1008 nan 0.0010 0.0002
## 320 1.0896 nan 0.0010 0.0002
## 340 1.0785 nan 0.0010 0.0002
## 360 1.0680 nan 0.0010 0.0002
## 380 1.0576 nan 0.0010 0.0002
## 400 1.0476 nan 0.0010 0.0002
## 420 1.0376 nan 0.0010 0.0002
## 440 1.0281 nan 0.0010 0.0002
## 460 1.0190 nan 0.0010 0.0002
## 480 1.0100 nan 0.0010 0.0002
## 500 1.0012 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2515 nan 0.0010 0.0004
## 100 1.2357 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2058 nan 0.0010 0.0003
## 160 1.1917 nan 0.0010 0.0003
## 180 1.1776 nan 0.0010 0.0003
## 200 1.1644 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
## 260 1.1265 nan 0.0010 0.0003
## 280 1.1146 nan 0.0010 0.0002
## 300 1.1031 nan 0.0010 0.0003
## 320 1.0918 nan 0.0010 0.0003
## 340 1.0808 nan 0.0010 0.0002
## 360 1.0702 nan 0.0010 0.0002
## 380 1.0599 nan 0.0010 0.0002
## 400 1.0500 nan 0.0010 0.0002
## 420 1.0400 nan 0.0010 0.0002
## 440 1.0304 nan 0.0010 0.0002
## 460 1.0212 nan 0.0010 0.0002
## 480 1.0123 nan 0.0010 0.0002
## 500 1.0036 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2375 nan 0.0010 0.0003
## 120 1.2223 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1543 nan 0.0010 0.0003
## 240 1.1417 nan 0.0010 0.0002
## 260 1.1295 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0003
## 300 1.1061 nan 0.0010 0.0002
## 320 1.0951 nan 0.0010 0.0003
## 340 1.0841 nan 0.0010 0.0003
## 360 1.0735 nan 0.0010 0.0002
## 380 1.0633 nan 0.0010 0.0002
## 400 1.0533 nan 0.0010 0.0002
## 420 1.0436 nan 0.0010 0.0002
## 440 1.0343 nan 0.0010 0.0002
## 460 1.0251 nan 0.0010 0.0002
## 480 1.0161 nan 0.0010 0.0002
## 500 1.0072 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3156 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3127 nan 0.0010 0.0004
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2489 nan 0.0010 0.0004
## 100 1.2325 nan 0.0010 0.0004
## 120 1.2164 nan 0.0010 0.0004
## 140 1.2009 nan 0.0010 0.0003
## 160 1.1856 nan 0.0010 0.0003
## 180 1.1707 nan 0.0010 0.0003
## 200 1.1566 nan 0.0010 0.0003
## 220 1.1428 nan 0.0010 0.0003
## 240 1.1296 nan 0.0010 0.0003
## 260 1.1167 nan 0.0010 0.0003
## 280 1.1040 nan 0.0010 0.0003
## 300 1.0919 nan 0.0010 0.0002
## 320 1.0801 nan 0.0010 0.0003
## 340 1.0687 nan 0.0010 0.0002
## 360 1.0576 nan 0.0010 0.0002
## 380 1.0465 nan 0.0010 0.0002
## 400 1.0360 nan 0.0010 0.0002
## 420 1.0259 nan 0.0010 0.0002
## 440 1.0160 nan 0.0010 0.0002
## 460 1.0066 nan 0.0010 0.0002
## 480 0.9971 nan 0.0010 0.0002
## 500 0.9881 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3195 nan 0.0010 0.0005
## 2 1.3185 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0004
## 4 1.3166 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0005
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2487 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2163 nan 0.0010 0.0003
## 140 1.2013 nan 0.0010 0.0003
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1453 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1194 nan 0.0010 0.0003
## 280 1.1071 nan 0.0010 0.0003
## 300 1.0951 nan 0.0010 0.0002
## 320 1.0834 nan 0.0010 0.0003
## 340 1.0722 nan 0.0010 0.0002
## 360 1.0610 nan 0.0010 0.0002
## 380 1.0502 nan 0.0010 0.0002
## 400 1.0399 nan 0.0010 0.0002
## 420 1.0295 nan 0.0010 0.0003
## 440 1.0196 nan 0.0010 0.0002
## 460 1.0100 nan 0.0010 0.0002
## 480 1.0006 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0004
## 80 1.2504 nan 0.0010 0.0004
## 100 1.2341 nan 0.0010 0.0004
## 120 1.2187 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
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## 220 1.1476 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
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## 280 1.1101 nan 0.0010 0.0003
## 300 1.0984 nan 0.0010 0.0003
## 320 1.0867 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0003
## 360 1.0647 nan 0.0010 0.0003
## 380 1.0542 nan 0.0010 0.0002
## 400 1.0439 nan 0.0010 0.0002
## 420 1.0338 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0145 nan 0.0010 0.0002
## 480 1.0052 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0040
## 4 1.2845 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0038
## 6 1.2686 nan 0.0100 0.0041
## 7 1.2606 nan 0.0100 0.0036
## 8 1.2530 nan 0.0100 0.0033
## 9 1.2450 nan 0.0100 0.0037
## 10 1.2376 nan 0.0100 0.0034
## 20 1.1697 nan 0.0100 0.0032
## 40 1.0623 nan 0.0100 0.0018
## 60 0.9793 nan 0.0100 0.0017
## 80 0.9151 nan 0.0100 0.0012
## 100 0.8631 nan 0.0100 0.0008
## 120 0.8212 nan 0.0100 0.0007
## 140 0.7862 nan 0.0100 0.0008
## 160 0.7559 nan 0.0100 0.0005
## 180 0.7292 nan 0.0100 0.0004
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## 260 0.6523 nan 0.0100 0.0001
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## 320 0.6120 nan 0.0100 0.0000
## 340 0.5996 nan 0.0100 0.0001
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## 380 0.5765 nan 0.0100 0.0001
## 400 0.5665 nan 0.0100 -0.0001
## 420 0.5572 nan 0.0100 -0.0001
## 440 0.5484 nan 0.0100 0.0001
## 460 0.5386 nan 0.0100 0.0000
## 480 0.5296 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0037
## 2 1.3037 nan 0.0100 0.0043
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2872 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0032
## 6 1.2709 nan 0.0100 0.0039
## 7 1.2627 nan 0.0100 0.0039
## 8 1.2547 nan 0.0100 0.0035
## 9 1.2470 nan 0.0100 0.0038
## 10 1.2390 nan 0.0100 0.0036
## 20 1.1732 nan 0.0100 0.0029
## 40 1.0646 nan 0.0100 0.0020
## 60 0.9808 nan 0.0100 0.0014
## 80 0.9160 nan 0.0100 0.0013
## 100 0.8636 nan 0.0100 0.0010
## 120 0.8213 nan 0.0100 0.0006
## 140 0.7858 nan 0.0100 0.0006
## 160 0.7551 nan 0.0100 0.0003
## 180 0.7302 nan 0.0100 0.0003
## 200 0.7077 nan 0.0100 0.0002
## 220 0.6870 nan 0.0100 0.0002
## 240 0.6699 nan 0.0100 0.0000
## 260 0.6544 nan 0.0100 0.0001
## 280 0.6402 nan 0.0100 0.0001
## 300 0.6277 nan 0.0100 0.0000
## 320 0.6156 nan 0.0100 -0.0000
## 340 0.6039 nan 0.0100 -0.0001
## 360 0.5921 nan 0.0100 0.0000
## 380 0.5819 nan 0.0100 -0.0000
## 400 0.5713 nan 0.0100 0.0001
## 420 0.5621 nan 0.0100 -0.0001
## 440 0.5524 nan 0.0100 -0.0000
## 460 0.5430 nan 0.0100 -0.0000
## 480 0.5345 nan 0.0100 -0.0000
## 500 0.5261 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0038
## 2 1.3045 nan 0.0100 0.0037
## 3 1.2967 nan 0.0100 0.0036
## 4 1.2886 nan 0.0100 0.0033
## 5 1.2801 nan 0.0100 0.0041
## 6 1.2729 nan 0.0100 0.0034
## 7 1.2652 nan 0.0100 0.0038
## 8 1.2581 nan 0.0100 0.0031
## 9 1.2502 nan 0.0100 0.0036
## 10 1.2435 nan 0.0100 0.0032
## 20 1.1751 nan 0.0100 0.0029
## 40 1.0669 nan 0.0100 0.0022
## 60 0.9828 nan 0.0100 0.0014
## 80 0.9167 nan 0.0100 0.0010
## 100 0.8652 nan 0.0100 0.0008
## 120 0.8216 nan 0.0100 0.0007
## 140 0.7876 nan 0.0100 0.0005
## 160 0.7590 nan 0.0100 0.0003
## 180 0.7342 nan 0.0100 0.0000
## 200 0.7125 nan 0.0100 0.0002
## 220 0.6945 nan 0.0100 0.0000
## 240 0.6775 nan 0.0100 0.0001
## 260 0.6623 nan 0.0100 -0.0001
## 280 0.6478 nan 0.0100 0.0001
## 300 0.6344 nan 0.0100 -0.0001
## 320 0.6223 nan 0.0100 -0.0001
## 340 0.6105 nan 0.0100 0.0001
## 360 0.6003 nan 0.0100 -0.0001
## 380 0.5901 nan 0.0100 -0.0000
## 400 0.5810 nan 0.0100 -0.0000
## 420 0.5715 nan 0.0100 -0.0001
## 440 0.5638 nan 0.0100 0.0001
## 460 0.5555 nan 0.0100 0.0000
## 480 0.5476 nan 0.0100 -0.0000
## 500 0.5399 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3017 nan 0.0100 0.0046
## 3 1.2927 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0033
## 5 1.2755 nan 0.0100 0.0041
## 6 1.2670 nan 0.0100 0.0039
## 7 1.2587 nan 0.0100 0.0039
## 8 1.2502 nan 0.0100 0.0039
## 9 1.2419 nan 0.0100 0.0036
## 10 1.2350 nan 0.0100 0.0031
## 20 1.1629 nan 0.0100 0.0031
## 40 1.0474 nan 0.0100 0.0020
## 60 0.9599 nan 0.0100 0.0017
## 80 0.8905 nan 0.0100 0.0009
## 100 0.8373 nan 0.0100 0.0008
## 120 0.7937 nan 0.0100 0.0006
## 140 0.7572 nan 0.0100 0.0006
## 160 0.7253 nan 0.0100 0.0004
## 180 0.6979 nan 0.0100 0.0001
## 200 0.6743 nan 0.0100 0.0002
## 220 0.6530 nan 0.0100 0.0001
## 240 0.6337 nan 0.0100 0.0002
## 260 0.6164 nan 0.0100 -0.0001
## 280 0.6000 nan 0.0100 0.0001
## 300 0.5854 nan 0.0100 0.0001
## 320 0.5706 nan 0.0100 0.0002
## 340 0.5580 nan 0.0100 0.0001
## 360 0.5453 nan 0.0100 -0.0000
## 380 0.5320 nan 0.0100 0.0001
## 400 0.5210 nan 0.0100 0.0001
## 420 0.5102 nan 0.0100 0.0002
## 440 0.4999 nan 0.0100 0.0000
## 460 0.4903 nan 0.0100 -0.0001
## 480 0.4808 nan 0.0100 -0.0000
## 500 0.4720 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0046
## 2 1.3016 nan 0.0100 0.0043
## 3 1.2925 nan 0.0100 0.0034
## 4 1.2841 nan 0.0100 0.0038
## 5 1.2753 nan 0.0100 0.0039
## 6 1.2670 nan 0.0100 0.0033
## 7 1.2588 nan 0.0100 0.0036
## 8 1.2514 nan 0.0100 0.0030
## 9 1.2429 nan 0.0100 0.0038
## 10 1.2338 nan 0.0100 0.0037
## 20 1.1625 nan 0.0100 0.0026
## 40 1.0489 nan 0.0100 0.0022
## 60 0.9607 nan 0.0100 0.0017
## 80 0.8948 nan 0.0100 0.0010
## 100 0.8406 nan 0.0100 0.0010
## 120 0.7963 nan 0.0100 0.0007
## 140 0.7606 nan 0.0100 0.0001
## 160 0.7303 nan 0.0100 0.0004
## 180 0.7029 nan 0.0100 0.0002
## 200 0.6790 nan 0.0100 0.0003
## 220 0.6582 nan 0.0100 0.0002
## 240 0.6396 nan 0.0100 0.0002
## 260 0.6231 nan 0.0100 0.0002
## 280 0.6074 nan 0.0100 0.0000
## 300 0.5927 nan 0.0100 -0.0001
## 320 0.5777 nan 0.0100 0.0000
## 340 0.5658 nan 0.0100 0.0000
## 360 0.5539 nan 0.0100 0.0002
## 380 0.5419 nan 0.0100 0.0000
## 400 0.5308 nan 0.0100 0.0001
## 420 0.5201 nan 0.0100 0.0001
## 440 0.5096 nan 0.0100 -0.0000
## 460 0.4997 nan 0.0100 0.0001
## 480 0.4912 nan 0.0100 -0.0001
## 500 0.4820 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3023 nan 0.0100 0.0039
## 3 1.2941 nan 0.0100 0.0037
## 4 1.2851 nan 0.0100 0.0041
## 5 1.2769 nan 0.0100 0.0037
## 6 1.2682 nan 0.0100 0.0039
## 7 1.2600 nan 0.0100 0.0034
## 8 1.2521 nan 0.0100 0.0039
## 9 1.2442 nan 0.0100 0.0034
## 10 1.2364 nan 0.0100 0.0034
## 20 1.1671 nan 0.0100 0.0027
## 40 1.0524 nan 0.0100 0.0021
## 60 0.9658 nan 0.0100 0.0015
## 80 0.8994 nan 0.0100 0.0012
## 100 0.8458 nan 0.0100 0.0009
## 120 0.8017 nan 0.0100 0.0008
## 140 0.7646 nan 0.0100 0.0006
## 160 0.7332 nan 0.0100 0.0003
## 180 0.7081 nan 0.0100 0.0003
## 200 0.6832 nan 0.0100 0.0002
## 220 0.6618 nan 0.0100 0.0001
## 240 0.6450 nan 0.0100 0.0000
## 260 0.6287 nan 0.0100 0.0001
## 280 0.6140 nan 0.0100 0.0002
## 300 0.6006 nan 0.0100 0.0001
## 320 0.5878 nan 0.0100 0.0001
## 340 0.5757 nan 0.0100 0.0001
## 360 0.5634 nan 0.0100 0.0001
## 380 0.5512 nan 0.0100 0.0001
## 400 0.5405 nan 0.0100 -0.0001
## 420 0.5302 nan 0.0100 0.0000
## 440 0.5202 nan 0.0100 -0.0002
## 460 0.5105 nan 0.0100 -0.0001
## 480 0.5020 nan 0.0100 -0.0000
## 500 0.4934 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3013 nan 0.0100 0.0042
## 3 1.2915 nan 0.0100 0.0044
## 4 1.2825 nan 0.0100 0.0041
## 5 1.2745 nan 0.0100 0.0034
## 6 1.2649 nan 0.0100 0.0043
## 7 1.2563 nan 0.0100 0.0037
## 8 1.2475 nan 0.0100 0.0039
## 9 1.2384 nan 0.0100 0.0042
## 10 1.2300 nan 0.0100 0.0032
## 20 1.1562 nan 0.0100 0.0029
## 40 1.0377 nan 0.0100 0.0022
## 60 0.9459 nan 0.0100 0.0019
## 80 0.8741 nan 0.0100 0.0012
## 100 0.8170 nan 0.0100 0.0010
## 120 0.7698 nan 0.0100 0.0009
## 140 0.7298 nan 0.0100 0.0005
## 160 0.6962 nan 0.0100 0.0003
## 180 0.6677 nan 0.0100 0.0003
## 200 0.6436 nan 0.0100 0.0002
## 220 0.6210 nan 0.0100 0.0001
## 240 0.5999 nan 0.0100 0.0000
## 260 0.5815 nan 0.0100 0.0002
## 280 0.5647 nan 0.0100 0.0001
## 300 0.5484 nan 0.0100 0.0000
## 320 0.5333 nan 0.0100 0.0001
## 340 0.5197 nan 0.0100 -0.0001
## 360 0.5058 nan 0.0100 -0.0001
## 380 0.4936 nan 0.0100 0.0001
## 400 0.4815 nan 0.0100 0.0000
## 420 0.4704 nan 0.0100 -0.0000
## 440 0.4584 nan 0.0100 -0.0001
## 460 0.4472 nan 0.0100 0.0000
## 480 0.4379 nan 0.0100 -0.0000
## 500 0.4287 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3006 nan 0.0100 0.0045
## 3 1.2910 nan 0.0100 0.0044
## 4 1.2825 nan 0.0100 0.0039
## 5 1.2739 nan 0.0100 0.0039
## 6 1.2649 nan 0.0100 0.0040
## 7 1.2559 nan 0.0100 0.0043
## 8 1.2478 nan 0.0100 0.0039
## 9 1.2393 nan 0.0100 0.0035
## 10 1.2315 nan 0.0100 0.0032
## 20 1.1578 nan 0.0100 0.0029
## 40 1.0380 nan 0.0100 0.0023
## 60 0.9469 nan 0.0100 0.0013
## 80 0.8761 nan 0.0100 0.0012
## 100 0.8180 nan 0.0100 0.0011
## 120 0.7719 nan 0.0100 0.0006
## 140 0.7343 nan 0.0100 0.0005
## 160 0.7021 nan 0.0100 0.0004
## 180 0.6742 nan 0.0100 0.0002
## 200 0.6497 nan 0.0100 0.0001
## 220 0.6277 nan 0.0100 0.0003
## 240 0.6069 nan 0.0100 0.0003
## 260 0.5883 nan 0.0100 0.0002
## 280 0.5709 nan 0.0100 0.0001
## 300 0.5554 nan 0.0100 0.0001
## 320 0.5397 nan 0.0100 -0.0001
## 340 0.5253 nan 0.0100 0.0000
## 360 0.5127 nan 0.0100 0.0000
## 380 0.5000 nan 0.0100 0.0000
## 400 0.4884 nan 0.0100 0.0002
## 420 0.4760 nan 0.0100 0.0001
## 440 0.4651 nan 0.0100 0.0000
## 460 0.4541 nan 0.0100 -0.0000
## 480 0.4436 nan 0.0100 -0.0000
## 500 0.4345 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2925 nan 0.0100 0.0039
## 4 1.2837 nan 0.0100 0.0038
## 5 1.2748 nan 0.0100 0.0041
## 6 1.2662 nan 0.0100 0.0037
## 7 1.2577 nan 0.0100 0.0037
## 8 1.2495 nan 0.0100 0.0034
## 9 1.2406 nan 0.0100 0.0041
## 10 1.2322 nan 0.0100 0.0040
## 20 1.1593 nan 0.0100 0.0029
## 40 1.0397 nan 0.0100 0.0023
## 60 0.9512 nan 0.0100 0.0016
## 80 0.8820 nan 0.0100 0.0011
## 100 0.8264 nan 0.0100 0.0009
## 120 0.7809 nan 0.0100 0.0003
## 140 0.7425 nan 0.0100 0.0004
## 160 0.7099 nan 0.0100 0.0006
## 180 0.6828 nan 0.0100 0.0004
## 200 0.6597 nan 0.0100 0.0003
## 220 0.6382 nan 0.0100 0.0003
## 240 0.6182 nan 0.0100 -0.0000
## 260 0.5991 nan 0.0100 0.0003
## 280 0.5824 nan 0.0100 -0.0000
## 300 0.5667 nan 0.0100 0.0001
## 320 0.5520 nan 0.0100 -0.0000
## 340 0.5389 nan 0.0100 0.0001
## 360 0.5258 nan 0.0100 -0.0000
## 380 0.5128 nan 0.0100 0.0001
## 400 0.5015 nan 0.0100 0.0002
## 420 0.4906 nan 0.0100 -0.0001
## 440 0.4803 nan 0.0100 0.0001
## 460 0.4696 nan 0.0100 -0.0000
## 480 0.4597 nan 0.0100 -0.0000
## 500 0.4494 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2384 nan 0.1000 0.0431
## 2 1.1693 nan 0.1000 0.0291
## 3 1.1079 nan 0.1000 0.0285
## 4 1.0560 nan 0.1000 0.0217
## 5 1.0152 nan 0.1000 0.0170
## 6 0.9729 nan 0.1000 0.0166
## 7 0.9383 nan 0.1000 0.0139
## 8 0.9130 nan 0.1000 0.0087
## 9 0.8868 nan 0.1000 0.0110
## 10 0.8621 nan 0.1000 0.0093
## 20 0.7071 nan 0.1000 0.0022
## 40 0.5644 nan 0.1000 -0.0002
## 60 0.4820 nan 0.1000 -0.0009
## 80 0.4195 nan 0.1000 -0.0007
## 100 0.3751 nan 0.1000 -0.0011
## 120 0.3285 nan 0.1000 -0.0007
## 140 0.2930 nan 0.1000 -0.0002
## 160 0.2609 nan 0.1000 -0.0003
## 180 0.2329 nan 0.1000 0.0002
## 200 0.2109 nan 0.1000 -0.0008
## 220 0.1884 nan 0.1000 -0.0002
## 240 0.1725 nan 0.1000 -0.0001
## 260 0.1547 nan 0.1000 -0.0004
## 280 0.1406 nan 0.1000 -0.0002
## 300 0.1294 nan 0.1000 -0.0003
## 320 0.1181 nan 0.1000 -0.0004
## 340 0.1086 nan 0.1000 -0.0002
## 360 0.0996 nan 0.1000 -0.0005
## 380 0.0909 nan 0.1000 -0.0001
## 400 0.0833 nan 0.1000 -0.0005
## 420 0.0764 nan 0.1000 -0.0001
## 440 0.0696 nan 0.1000 -0.0001
## 460 0.0639 nan 0.1000 -0.0002
## 480 0.0587 nan 0.1000 -0.0002
## 500 0.0544 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0358
## 2 1.1624 nan 0.1000 0.0333
## 3 1.1126 nan 0.1000 0.0234
## 4 1.0607 nan 0.1000 0.0211
## 5 1.0143 nan 0.1000 0.0187
## 6 0.9817 nan 0.1000 0.0141
## 7 0.9481 nan 0.1000 0.0151
## 8 0.9182 nan 0.1000 0.0105
## 9 0.8900 nan 0.1000 0.0109
## 10 0.8650 nan 0.1000 0.0093
## 20 0.7143 nan 0.1000 0.0023
## 40 0.5757 nan 0.1000 0.0006
## 60 0.4900 nan 0.1000 -0.0007
## 80 0.4296 nan 0.1000 -0.0011
## 100 0.3731 nan 0.1000 -0.0003
## 120 0.3336 nan 0.1000 -0.0012
## 140 0.2985 nan 0.1000 -0.0007
## 160 0.2714 nan 0.1000 -0.0006
## 180 0.2404 nan 0.1000 -0.0007
## 200 0.2179 nan 0.1000 -0.0009
## 220 0.1975 nan 0.1000 -0.0004
## 240 0.1801 nan 0.1000 -0.0003
## 260 0.1646 nan 0.1000 -0.0008
## 280 0.1505 nan 0.1000 -0.0004
## 300 0.1388 nan 0.1000 -0.0003
## 320 0.1265 nan 0.1000 -0.0005
## 340 0.1155 nan 0.1000 -0.0003
## 360 0.1065 nan 0.1000 -0.0001
## 380 0.0977 nan 0.1000 0.0001
## 400 0.0890 nan 0.1000 -0.0003
## 420 0.0812 nan 0.1000 0.0000
## 440 0.0745 nan 0.1000 -0.0002
## 460 0.0690 nan 0.1000 -0.0000
## 480 0.0633 nan 0.1000 -0.0002
## 500 0.0589 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0364
## 2 1.1662 nan 0.1000 0.0274
## 3 1.1088 nan 0.1000 0.0243
## 4 1.0578 nan 0.1000 0.0252
## 5 1.0166 nan 0.1000 0.0174
## 6 0.9795 nan 0.1000 0.0148
## 7 0.9466 nan 0.1000 0.0149
## 8 0.9170 nan 0.1000 0.0116
## 9 0.8878 nan 0.1000 0.0113
## 10 0.8660 nan 0.1000 0.0071
## 20 0.7104 nan 0.1000 0.0038
## 40 0.5861 nan 0.1000 -0.0001
## 60 0.5076 nan 0.1000 -0.0020
## 80 0.4489 nan 0.1000 -0.0010
## 100 0.3963 nan 0.1000 -0.0013
## 120 0.3562 nan 0.1000 -0.0005
## 140 0.3132 nan 0.1000 -0.0007
## 160 0.2816 nan 0.1000 -0.0001
## 180 0.2546 nan 0.1000 -0.0008
## 200 0.2304 nan 0.1000 -0.0011
## 220 0.2077 nan 0.1000 -0.0005
## 240 0.1923 nan 0.1000 -0.0008
## 260 0.1766 nan 0.1000 -0.0005
## 280 0.1644 nan 0.1000 -0.0009
## 300 0.1503 nan 0.1000 -0.0007
## 320 0.1386 nan 0.1000 -0.0005
## 340 0.1275 nan 0.1000 -0.0004
## 360 0.1160 nan 0.1000 -0.0003
## 380 0.1081 nan 0.1000 -0.0003
## 400 0.0998 nan 0.1000 -0.0003
## 420 0.0924 nan 0.1000 -0.0003
## 440 0.0857 nan 0.1000 -0.0002
## 460 0.0789 nan 0.1000 -0.0001
## 480 0.0732 nan 0.1000 -0.0002
## 500 0.0676 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2305 nan 0.1000 0.0379
## 2 1.1606 nan 0.1000 0.0328
## 3 1.1010 nan 0.1000 0.0268
## 4 1.0459 nan 0.1000 0.0245
## 5 0.9947 nan 0.1000 0.0220
## 6 0.9569 nan 0.1000 0.0120
## 7 0.9196 nan 0.1000 0.0140
## 8 0.8895 nan 0.1000 0.0131
## 9 0.8597 nan 0.1000 0.0118
## 10 0.8343 nan 0.1000 0.0096
## 20 0.6732 nan 0.1000 0.0057
## 40 0.5263 nan 0.1000 -0.0015
## 60 0.4333 nan 0.1000 0.0011
## 80 0.3679 nan 0.1000 0.0002
## 100 0.3060 nan 0.1000 -0.0013
## 120 0.2614 nan 0.1000 -0.0001
## 140 0.2277 nan 0.1000 -0.0009
## 160 0.1969 nan 0.1000 -0.0007
## 180 0.1717 nan 0.1000 -0.0012
## 200 0.1511 nan 0.1000 -0.0003
## 220 0.1350 nan 0.1000 -0.0001
## 240 0.1209 nan 0.1000 -0.0003
## 260 0.1087 nan 0.1000 -0.0003
## 280 0.0968 nan 0.1000 -0.0004
## 300 0.0863 nan 0.1000 0.0001
## 320 0.0765 nan 0.1000 -0.0000
## 340 0.0694 nan 0.1000 -0.0003
## 360 0.0611 nan 0.1000 0.0000
## 380 0.0549 nan 0.1000 -0.0002
## 400 0.0492 nan 0.1000 -0.0002
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## 460 0.0362 nan 0.1000 -0.0000
## 480 0.0326 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2335 nan 0.1000 0.0402
## 2 1.1649 nan 0.1000 0.0314
## 3 1.0973 nan 0.1000 0.0310
## 4 1.0445 nan 0.1000 0.0232
## 5 0.9977 nan 0.1000 0.0190
## 6 0.9551 nan 0.1000 0.0182
## 7 0.9189 nan 0.1000 0.0118
## 8 0.8910 nan 0.1000 0.0090
## 9 0.8642 nan 0.1000 0.0111
## 10 0.8401 nan 0.1000 0.0089
## 20 0.6810 nan 0.1000 0.0015
## 40 0.5421 nan 0.1000 -0.0001
## 60 0.4520 nan 0.1000 -0.0003
## 80 0.3825 nan 0.1000 -0.0018
## 100 0.3270 nan 0.1000 -0.0006
## 120 0.2832 nan 0.1000 -0.0004
## 140 0.2428 nan 0.1000 -0.0007
## 160 0.2131 nan 0.1000 -0.0003
## 180 0.1859 nan 0.1000 -0.0007
## 200 0.1638 nan 0.1000 -0.0006
## 220 0.1429 nan 0.1000 -0.0007
## 240 0.1286 nan 0.1000 -0.0006
## 260 0.1132 nan 0.1000 -0.0000
## 280 0.0986 nan 0.1000 -0.0003
## 300 0.0888 nan 0.1000 -0.0002
## 320 0.0798 nan 0.1000 -0.0001
## 340 0.0712 nan 0.1000 -0.0003
## 360 0.0637 nan 0.1000 -0.0000
## 380 0.0579 nan 0.1000 -0.0002
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## 460 0.0377 nan 0.1000 -0.0001
## 480 0.0341 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0396
## 2 1.1614 nan 0.1000 0.0327
## 3 1.0967 nan 0.1000 0.0287
## 4 1.0451 nan 0.1000 0.0228
## 5 1.0012 nan 0.1000 0.0171
## 6 0.9591 nan 0.1000 0.0170
## 7 0.9218 nan 0.1000 0.0145
## 8 0.8938 nan 0.1000 0.0117
## 9 0.8646 nan 0.1000 0.0106
## 10 0.8433 nan 0.1000 0.0080
## 20 0.6923 nan 0.1000 0.0019
## 40 0.5484 nan 0.1000 0.0001
## 60 0.4579 nan 0.1000 -0.0005
## 80 0.3872 nan 0.1000 -0.0007
## 100 0.3368 nan 0.1000 -0.0004
## 120 0.2916 nan 0.1000 0.0001
## 140 0.2577 nan 0.1000 -0.0004
## 160 0.2281 nan 0.1000 -0.0006
## 180 0.1987 nan 0.1000 -0.0009
## 200 0.1746 nan 0.1000 -0.0006
## 220 0.1562 nan 0.1000 -0.0003
## 240 0.1404 nan 0.1000 -0.0006
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## 280 0.1114 nan 0.1000 -0.0004
## 300 0.1003 nan 0.1000 -0.0005
## 320 0.0907 nan 0.1000 -0.0005
## 340 0.0813 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0448
## 2 1.1526 nan 0.1000 0.0362
## 3 1.0851 nan 0.1000 0.0291
## 4 1.0251 nan 0.1000 0.0263
## 5 0.9742 nan 0.1000 0.0198
## 6 0.9347 nan 0.1000 0.0174
## 7 0.8966 nan 0.1000 0.0163
## 8 0.8654 nan 0.1000 0.0133
## 9 0.8368 nan 0.1000 0.0103
## 10 0.8085 nan 0.1000 0.0093
## 20 0.6385 nan 0.1000 0.0035
## 40 0.4912 nan 0.1000 0.0003
## 60 0.3941 nan 0.1000 -0.0011
## 80 0.3160 nan 0.1000 0.0004
## 100 0.2621 nan 0.1000 0.0001
## 120 0.2186 nan 0.1000 -0.0005
## 140 0.1877 nan 0.1000 -0.0004
## 160 0.1568 nan 0.1000 -0.0004
## 180 0.1366 nan 0.1000 -0.0004
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## 220 0.1000 nan 0.1000 -0.0001
## 240 0.0871 nan 0.1000 -0.0002
## 260 0.0771 nan 0.1000 -0.0004
## 280 0.0682 nan 0.1000 -0.0003
## 300 0.0597 nan 0.1000 0.0000
## 320 0.0518 nan 0.1000 -0.0000
## 340 0.0453 nan 0.1000 -0.0001
## 360 0.0392 nan 0.1000 -0.0001
## 380 0.0345 nan 0.1000 0.0001
## 400 0.0302 nan 0.1000 -0.0000
## 420 0.0266 nan 0.1000 -0.0000
## 440 0.0233 nan 0.1000 -0.0001
## 460 0.0205 nan 0.1000 -0.0000
## 480 0.0179 nan 0.1000 -0.0001
## 500 0.0159 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2242 nan 0.1000 0.0420
## 2 1.1512 nan 0.1000 0.0342
## 3 1.0894 nan 0.1000 0.0290
## 4 1.0329 nan 0.1000 0.0238
## 5 0.9820 nan 0.1000 0.0205
## 6 0.9435 nan 0.1000 0.0146
## 7 0.9106 nan 0.1000 0.0125
## 8 0.8719 nan 0.1000 0.0148
## 9 0.8426 nan 0.1000 0.0131
## 10 0.8164 nan 0.1000 0.0092
## 20 0.6527 nan 0.1000 0.0023
## 40 0.4884 nan 0.1000 -0.0002
## 60 0.4010 nan 0.1000 0.0013
## 80 0.3218 nan 0.1000 -0.0002
## 100 0.2652 nan 0.1000 -0.0010
## 120 0.2301 nan 0.1000 -0.0010
## 140 0.1940 nan 0.1000 -0.0005
## 160 0.1695 nan 0.1000 -0.0006
## 180 0.1452 nan 0.1000 -0.0006
## 200 0.1241 nan 0.1000 -0.0002
## 220 0.1071 nan 0.1000 -0.0006
## 240 0.0943 nan 0.1000 0.0001
## 260 0.0827 nan 0.1000 -0.0005
## 280 0.0729 nan 0.1000 -0.0006
## 300 0.0635 nan 0.1000 -0.0003
## 320 0.0553 nan 0.1000 -0.0002
## 340 0.0489 nan 0.1000 -0.0001
## 360 0.0429 nan 0.1000 0.0000
## 380 0.0374 nan 0.1000 -0.0001
## 400 0.0325 nan 0.1000 -0.0001
## 420 0.0288 nan 0.1000 -0.0001
## 440 0.0254 nan 0.1000 -0.0000
## 460 0.0225 nan 0.1000 -0.0001
## 480 0.0199 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0423
## 2 1.1583 nan 0.1000 0.0353
## 3 1.0942 nan 0.1000 0.0261
## 4 1.0363 nan 0.1000 0.0253
## 5 0.9896 nan 0.1000 0.0192
## 6 0.9486 nan 0.1000 0.0170
## 7 0.9115 nan 0.1000 0.0150
## 8 0.8810 nan 0.1000 0.0126
## 9 0.8504 nan 0.1000 0.0116
## 10 0.8206 nan 0.1000 0.0121
## 20 0.6615 nan 0.1000 0.0024
## 40 0.5043 nan 0.1000 -0.0009
## 60 0.4036 nan 0.1000 -0.0005
## 80 0.3387 nan 0.1000 -0.0008
## 100 0.2872 nan 0.1000 -0.0013
## 120 0.2439 nan 0.1000 0.0003
## 140 0.2083 nan 0.1000 -0.0005
## 160 0.1770 nan 0.1000 -0.0005
## 180 0.1524 nan 0.1000 -0.0005
## 200 0.1315 nan 0.1000 -0.0004
## 220 0.1153 nan 0.1000 -0.0004
## 240 0.1014 nan 0.1000 -0.0004
## 260 0.0891 nan 0.1000 -0.0004
## 280 0.0792 nan 0.1000 -0.0001
## 300 0.0693 nan 0.1000 -0.0005
## 320 0.0602 nan 0.1000 -0.0001
## 340 0.0534 nan 0.1000 -0.0003
## 360 0.0472 nan 0.1000 -0.0002
## 380 0.0418 nan 0.1000 -0.0001
## 400 0.0370 nan 0.1000 -0.0001
## 420 0.0333 nan 0.1000 -0.0002
## 440 0.0294 nan 0.1000 -0.0001
## 460 0.0260 nan 0.1000 -0.0000
## 480 0.0229 nan 0.1000 -0.0001
## 500 0.0206 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3044 nan 0.0010 0.0003
## 40 1.2881 nan 0.0010 0.0003
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2275 nan 0.0010 0.0003
## 140 1.2137 nan 0.0010 0.0003
## 160 1.2002 nan 0.0010 0.0003
## 180 1.1876 nan 0.0010 0.0003
## 200 1.1752 nan 0.0010 0.0002
## 220 1.1629 nan 0.0010 0.0003
## 240 1.1507 nan 0.0010 0.0003
## 260 1.1392 nan 0.0010 0.0003
## 280 1.1278 nan 0.0010 0.0002
## 300 1.1172 nan 0.0010 0.0002
## 320 1.1065 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0860 nan 0.0010 0.0002
## 380 1.0762 nan 0.0010 0.0002
## 400 1.0667 nan 0.0010 0.0002
## 420 1.0573 nan 0.0010 0.0002
## 440 1.0482 nan 0.0010 0.0002
## 460 1.0394 nan 0.0010 0.0002
## 480 1.0310 nan 0.0010 0.0001
## 500 1.0228 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0004
## 80 1.2566 nan 0.0010 0.0004
## 100 1.2416 nan 0.0010 0.0003
## 120 1.2276 nan 0.0010 0.0003
## 140 1.2137 nan 0.0010 0.0003
## 160 1.2003 nan 0.0010 0.0003
## 180 1.1872 nan 0.0010 0.0003
## 200 1.1743 nan 0.0010 0.0003
## 220 1.1619 nan 0.0010 0.0003
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1386 nan 0.0010 0.0003
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1164 nan 0.0010 0.0003
## 320 1.1061 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0858 nan 0.0010 0.0003
## 380 1.0759 nan 0.0010 0.0002
## 400 1.0663 nan 0.0010 0.0002
## 420 1.0571 nan 0.0010 0.0002
## 440 1.0481 nan 0.0010 0.0002
## 460 1.0393 nan 0.0010 0.0002
## 480 1.0307 nan 0.0010 0.0002
## 500 1.0221 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2727 nan 0.0010 0.0003
## 80 1.2576 nan 0.0010 0.0003
## 100 1.2428 nan 0.0010 0.0003
## 120 1.2288 nan 0.0010 0.0003
## 140 1.2149 nan 0.0010 0.0003
## 160 1.2015 nan 0.0010 0.0003
## 180 1.1883 nan 0.0010 0.0003
## 200 1.1761 nan 0.0010 0.0003
## 220 1.1638 nan 0.0010 0.0003
## 240 1.1521 nan 0.0010 0.0003
## 260 1.1405 nan 0.0010 0.0002
## 280 1.1294 nan 0.0010 0.0003
## 300 1.1187 nan 0.0010 0.0002
## 320 1.1081 nan 0.0010 0.0002
## 340 1.0979 nan 0.0010 0.0003
## 360 1.0881 nan 0.0010 0.0002
## 380 1.0783 nan 0.0010 0.0002
## 400 1.0690 nan 0.0010 0.0002
## 420 1.0598 nan 0.0010 0.0002
## 440 1.0509 nan 0.0010 0.0002
## 460 1.0420 nan 0.0010 0.0002
## 480 1.0335 nan 0.0010 0.0002
## 500 1.0253 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0003
## 80 1.2521 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1907 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
## 200 1.1636 nan 0.0010 0.0003
## 220 1.1503 nan 0.0010 0.0003
## 240 1.1374 nan 0.0010 0.0002
## 260 1.1252 nan 0.0010 0.0002
## 280 1.1132 nan 0.0010 0.0003
## 300 1.1015 nan 0.0010 0.0003
## 320 1.0903 nan 0.0010 0.0002
## 340 1.0795 nan 0.0010 0.0002
## 360 1.0688 nan 0.0010 0.0002
## 380 1.0584 nan 0.0010 0.0002
## 400 1.0485 nan 0.0010 0.0002
## 420 1.0386 nan 0.0010 0.0002
## 440 1.0292 nan 0.0010 0.0002
## 460 1.0199 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
## 500 1.0024 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2362 nan 0.0010 0.0004
## 120 1.2211 nan 0.0010 0.0004
## 140 1.2064 nan 0.0010 0.0003
## 160 1.1919 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1641 nan 0.0010 0.0003
## 220 1.1510 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0003
## 260 1.1260 nan 0.0010 0.0003
## 280 1.1141 nan 0.0010 0.0003
## 300 1.1024 nan 0.0010 0.0003
## 320 1.0912 nan 0.0010 0.0002
## 340 1.0801 nan 0.0010 0.0002
## 360 1.0695 nan 0.0010 0.0002
## 380 1.0592 nan 0.0010 0.0002
## 400 1.0493 nan 0.0010 0.0002
## 420 1.0396 nan 0.0010 0.0002
## 440 1.0302 nan 0.0010 0.0002
## 460 1.0210 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0032 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0005
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0003
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2528 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0004
## 120 1.2225 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1671 nan 0.0010 0.0003
## 220 1.1542 nan 0.0010 0.0003
## 240 1.1416 nan 0.0010 0.0003
## 260 1.1296 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0002
## 300 1.1065 nan 0.0010 0.0002
## 320 1.0955 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0002
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0638 nan 0.0010 0.0003
## 400 1.0540 nan 0.0010 0.0002
## 420 1.0442 nan 0.0010 0.0002
## 440 1.0349 nan 0.0010 0.0002
## 460 1.0257 nan 0.0010 0.0002
## 480 1.0169 nan 0.0010 0.0001
## 500 1.0081 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2660 nan 0.0010 0.0004
## 80 1.2487 nan 0.0010 0.0004
## 100 1.2321 nan 0.0010 0.0004
## 120 1.2159 nan 0.0010 0.0004
## 140 1.2001 nan 0.0010 0.0003
## 160 1.1848 nan 0.0010 0.0003
## 180 1.1703 nan 0.0010 0.0003
## 200 1.1565 nan 0.0010 0.0003
## 220 1.1432 nan 0.0010 0.0002
## 240 1.1300 nan 0.0010 0.0003
## 260 1.1170 nan 0.0010 0.0003
## 280 1.1048 nan 0.0010 0.0003
## 300 1.0927 nan 0.0010 0.0003
## 320 1.0811 nan 0.0010 0.0003
## 340 1.0697 nan 0.0010 0.0002
## 360 1.0587 nan 0.0010 0.0003
## 380 1.0480 nan 0.0010 0.0002
## 400 1.0375 nan 0.0010 0.0002
## 420 1.0273 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0074 nan 0.0010 0.0002
## 480 0.9981 nan 0.0010 0.0002
## 500 0.9888 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2488 nan 0.0010 0.0004
## 100 1.2322 nan 0.0010 0.0004
## 120 1.2163 nan 0.0010 0.0004
## 140 1.2009 nan 0.0010 0.0003
## 160 1.1859 nan 0.0010 0.0004
## 180 1.1717 nan 0.0010 0.0003
## 200 1.1575 nan 0.0010 0.0003
## 220 1.1442 nan 0.0010 0.0003
## 240 1.1310 nan 0.0010 0.0003
## 260 1.1183 nan 0.0010 0.0003
## 280 1.1061 nan 0.0010 0.0003
## 300 1.0943 nan 0.0010 0.0003
## 320 1.0826 nan 0.0010 0.0002
## 340 1.0711 nan 0.0010 0.0002
## 360 1.0600 nan 0.0010 0.0002
## 380 1.0492 nan 0.0010 0.0002
## 400 1.0387 nan 0.0010 0.0002
## 420 1.0287 nan 0.0010 0.0002
## 440 1.0187 nan 0.0010 0.0002
## 460 1.0091 nan 0.0010 0.0002
## 480 0.9994 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0005
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2344 nan 0.0010 0.0004
## 120 1.2185 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1887 nan 0.0010 0.0002
## 180 1.1744 nan 0.0010 0.0004
## 200 1.1603 nan 0.0010 0.0003
## 220 1.1469 nan 0.0010 0.0003
## 240 1.1337 nan 0.0010 0.0003
## 260 1.1215 nan 0.0010 0.0002
## 280 1.1095 nan 0.0010 0.0003
## 300 1.0980 nan 0.0010 0.0003
## 320 1.0866 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0002
## 360 1.0645 nan 0.0010 0.0002
## 380 1.0538 nan 0.0010 0.0002
## 400 1.0438 nan 0.0010 0.0002
## 420 1.0338 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0145 nan 0.0010 0.0002
## 480 1.0051 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0036
## 2 1.3043 nan 0.0100 0.0038
## 3 1.2965 nan 0.0100 0.0037
## 4 1.2880 nan 0.0100 0.0040
## 5 1.2797 nan 0.0100 0.0034
## 6 1.2715 nan 0.0100 0.0037
## 7 1.2641 nan 0.0100 0.0032
## 8 1.2567 nan 0.0100 0.0032
## 9 1.2483 nan 0.0100 0.0040
## 10 1.2410 nan 0.0100 0.0034
## 20 1.1743 nan 0.0100 0.0029
## 40 1.0676 nan 0.0100 0.0020
## 60 0.9848 nan 0.0100 0.0016
## 80 0.9201 nan 0.0100 0.0009
## 100 0.8681 nan 0.0100 0.0008
## 120 0.8241 nan 0.0100 0.0008
## 140 0.7877 nan 0.0100 0.0004
## 160 0.7589 nan 0.0100 0.0003
## 180 0.7327 nan 0.0100 0.0004
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## 220 0.6915 nan 0.0100 0.0002
## 240 0.6733 nan 0.0100 0.0002
## 260 0.6579 nan 0.0100 -0.0000
## 280 0.6437 nan 0.0100 0.0000
## 300 0.6294 nan 0.0100 -0.0000
## 320 0.6175 nan 0.0100 0.0000
## 340 0.6059 nan 0.0100 -0.0000
## 360 0.5942 nan 0.0100 0.0001
## 380 0.5831 nan 0.0100 -0.0001
## 400 0.5727 nan 0.0100 0.0000
## 420 0.5636 nan 0.0100 -0.0001
## 440 0.5550 nan 0.0100 -0.0001
## 460 0.5456 nan 0.0100 -0.0002
## 480 0.5364 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3038 nan 0.0100 0.0040
## 3 1.2952 nan 0.0100 0.0039
## 4 1.2862 nan 0.0100 0.0039
## 5 1.2779 nan 0.0100 0.0039
## 6 1.2701 nan 0.0100 0.0036
## 7 1.2626 nan 0.0100 0.0036
## 8 1.2554 nan 0.0100 0.0037
## 9 1.2484 nan 0.0100 0.0033
## 10 1.2413 nan 0.0100 0.0031
## 20 1.1728 nan 0.0100 0.0028
## 40 1.0656 nan 0.0100 0.0022
## 60 0.9837 nan 0.0100 0.0014
## 80 0.9212 nan 0.0100 0.0010
## 100 0.8692 nan 0.0100 0.0009
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## 160 0.7616 nan 0.0100 0.0003
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## 380 0.5868 nan 0.0100 0.0001
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## 420 0.5682 nan 0.0100 0.0000
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## 460 0.5498 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0044
## 2 1.3046 nan 0.0100 0.0032
## 3 1.2964 nan 0.0100 0.0032
## 4 1.2894 nan 0.0100 0.0032
## 5 1.2816 nan 0.0100 0.0035
## 6 1.2739 nan 0.0100 0.0037
## 7 1.2664 nan 0.0100 0.0032
## 8 1.2587 nan 0.0100 0.0037
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## 10 1.2443 nan 0.0100 0.0034
## 20 1.1771 nan 0.0100 0.0028
## 40 1.0681 nan 0.0100 0.0016
## 60 0.9860 nan 0.0100 0.0016
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## 280 0.6461 nan 0.0100 0.0000
## 300 0.6329 nan 0.0100 -0.0000
## 320 0.6206 nan 0.0100 0.0001
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## 360 0.5998 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3030 nan 0.0100 0.0045
## 3 1.2942 nan 0.0100 0.0040
## 4 1.2843 nan 0.0100 0.0040
## 5 1.2755 nan 0.0100 0.0037
## 6 1.2678 nan 0.0100 0.0032
## 7 1.2593 nan 0.0100 0.0036
## 8 1.2509 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0036
## 10 1.2358 nan 0.0100 0.0037
## 20 1.1614 nan 0.0100 0.0035
## 40 1.0484 nan 0.0100 0.0022
## 60 0.9598 nan 0.0100 0.0015
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## 160 0.7264 nan 0.0100 0.0004
## 180 0.6991 nan 0.0100 0.0003
## 200 0.6760 nan 0.0100 0.0004
## 220 0.6546 nan 0.0100 0.0002
## 240 0.6358 nan 0.0100 0.0000
## 260 0.6181 nan 0.0100 0.0001
## 280 0.6025 nan 0.0100 -0.0001
## 300 0.5877 nan 0.0100 0.0001
## 320 0.5743 nan 0.0100 0.0002
## 340 0.5621 nan 0.0100 -0.0001
## 360 0.5490 nan 0.0100 0.0001
## 380 0.5367 nan 0.0100 0.0001
## 400 0.5252 nan 0.0100 -0.0001
## 420 0.5144 nan 0.0100 -0.0001
## 440 0.5033 nan 0.0100 0.0000
## 460 0.4939 nan 0.0100 -0.0001
## 480 0.4845 nan 0.0100 0.0001
## 500 0.4752 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0045
## 2 1.3028 nan 0.0100 0.0043
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2857 nan 0.0100 0.0045
## 5 1.2778 nan 0.0100 0.0039
## 6 1.2702 nan 0.0100 0.0033
## 7 1.2616 nan 0.0100 0.0039
## 8 1.2531 nan 0.0100 0.0035
## 9 1.2456 nan 0.0100 0.0032
## 10 1.2370 nan 0.0100 0.0035
## 20 1.1643 nan 0.0100 0.0025
## 40 1.0497 nan 0.0100 0.0022
## 60 0.9622 nan 0.0100 0.0016
## 80 0.8935 nan 0.0100 0.0010
## 100 0.8390 nan 0.0100 0.0010
## 120 0.7947 nan 0.0100 0.0008
## 140 0.7577 nan 0.0100 0.0006
## 160 0.7267 nan 0.0100 0.0003
## 180 0.7005 nan 0.0100 0.0002
## 200 0.6763 nan 0.0100 0.0003
## 220 0.6556 nan 0.0100 0.0003
## 240 0.6364 nan 0.0100 0.0001
## 260 0.6203 nan 0.0100 0.0003
## 280 0.6040 nan 0.0100 0.0000
## 300 0.5900 nan 0.0100 0.0001
## 320 0.5762 nan 0.0100 -0.0001
## 340 0.5636 nan 0.0100 -0.0000
## 360 0.5513 nan 0.0100 0.0000
## 380 0.5399 nan 0.0100 -0.0001
## 400 0.5287 nan 0.0100 -0.0000
## 420 0.5182 nan 0.0100 -0.0000
## 440 0.5082 nan 0.0100 -0.0001
## 460 0.4987 nan 0.0100 0.0000
## 480 0.4899 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0045
## 2 1.3019 nan 0.0100 0.0045
## 3 1.2925 nan 0.0100 0.0041
## 4 1.2833 nan 0.0100 0.0042
## 5 1.2749 nan 0.0100 0.0038
## 6 1.2671 nan 0.0100 0.0037
## 7 1.2588 nan 0.0100 0.0034
## 8 1.2511 nan 0.0100 0.0035
## 9 1.2438 nan 0.0100 0.0032
## 10 1.2363 nan 0.0100 0.0032
## 20 1.1650 nan 0.0100 0.0032
## 40 1.0524 nan 0.0100 0.0019
## 60 0.9662 nan 0.0100 0.0017
## 80 0.8994 nan 0.0100 0.0012
## 100 0.8446 nan 0.0100 0.0010
## 120 0.7999 nan 0.0100 0.0006
## 140 0.7637 nan 0.0100 0.0005
## 160 0.7333 nan 0.0100 0.0005
## 180 0.7072 nan 0.0100 0.0003
## 200 0.6844 nan 0.0100 0.0002
## 220 0.6637 nan 0.0100 0.0002
## 240 0.6435 nan 0.0100 0.0001
## 260 0.6270 nan 0.0100 0.0002
## 280 0.6127 nan 0.0100 0.0001
## 300 0.5982 nan 0.0100 -0.0001
## 320 0.5849 nan 0.0100 0.0001
## 340 0.5713 nan 0.0100 -0.0001
## 360 0.5592 nan 0.0100 -0.0001
## 380 0.5485 nan 0.0100 -0.0001
## 400 0.5370 nan 0.0100 0.0001
## 420 0.5280 nan 0.0100 -0.0000
## 440 0.5181 nan 0.0100 0.0000
## 460 0.5093 nan 0.0100 -0.0000
## 480 0.4997 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0047
## 2 1.3013 nan 0.0100 0.0045
## 3 1.2932 nan 0.0100 0.0035
## 4 1.2838 nan 0.0100 0.0045
## 5 1.2754 nan 0.0100 0.0035
## 6 1.2657 nan 0.0100 0.0047
## 7 1.2570 nan 0.0100 0.0039
## 8 1.2489 nan 0.0100 0.0035
## 9 1.2413 nan 0.0100 0.0036
## 10 1.2336 nan 0.0100 0.0032
## 20 1.1599 nan 0.0100 0.0027
## 40 1.0382 nan 0.0100 0.0022
## 60 0.9469 nan 0.0100 0.0017
## 80 0.8752 nan 0.0100 0.0015
## 100 0.8192 nan 0.0100 0.0009
## 120 0.7730 nan 0.0100 0.0006
## 140 0.7325 nan 0.0100 0.0006
## 160 0.6996 nan 0.0100 0.0006
## 180 0.6705 nan 0.0100 0.0004
## 200 0.6442 nan 0.0100 0.0003
## 220 0.6215 nan 0.0100 0.0000
## 240 0.6020 nan 0.0100 0.0001
## 260 0.5830 nan 0.0100 -0.0000
## 280 0.5658 nan 0.0100 -0.0002
## 300 0.5506 nan 0.0100 0.0001
## 320 0.5350 nan 0.0100 0.0002
## 340 0.5211 nan 0.0100 -0.0000
## 360 0.5081 nan 0.0100 0.0000
## 380 0.4948 nan 0.0100 -0.0000
## 400 0.4831 nan 0.0100 -0.0000
## 420 0.4717 nan 0.0100 0.0000
## 440 0.4607 nan 0.0100 0.0000
## 460 0.4507 nan 0.0100 -0.0001
## 480 0.4407 nan 0.0100 -0.0000
## 500 0.4304 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0048
## 2 1.3005 nan 0.0100 0.0048
## 3 1.2913 nan 0.0100 0.0039
## 4 1.2819 nan 0.0100 0.0043
## 5 1.2734 nan 0.0100 0.0043
## 6 1.2640 nan 0.0100 0.0039
## 7 1.2549 nan 0.0100 0.0037
## 8 1.2461 nan 0.0100 0.0038
## 9 1.2379 nan 0.0100 0.0036
## 10 1.2301 nan 0.0100 0.0035
## 20 1.1560 nan 0.0100 0.0034
## 40 1.0385 nan 0.0100 0.0022
## 60 0.9476 nan 0.0100 0.0017
## 80 0.8764 nan 0.0100 0.0014
## 100 0.8208 nan 0.0100 0.0008
## 120 0.7747 nan 0.0100 0.0007
## 140 0.7360 nan 0.0100 0.0004
## 160 0.7032 nan 0.0100 0.0005
## 180 0.6747 nan 0.0100 0.0004
## 200 0.6506 nan 0.0100 0.0002
## 220 0.6271 nan 0.0100 0.0004
## 240 0.6070 nan 0.0100 0.0001
## 260 0.5893 nan 0.0100 0.0001
## 280 0.5725 nan 0.0100 0.0001
## 300 0.5565 nan 0.0100 0.0000
## 320 0.5415 nan 0.0100 0.0000
## 340 0.5280 nan 0.0100 -0.0000
## 360 0.5148 nan 0.0100 0.0000
## 380 0.5035 nan 0.0100 0.0001
## 400 0.4914 nan 0.0100 0.0001
## 420 0.4796 nan 0.0100 0.0000
## 440 0.4688 nan 0.0100 -0.0001
## 460 0.4580 nan 0.0100 -0.0000
## 480 0.4482 nan 0.0100 0.0001
## 500 0.4381 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0047
## 2 1.3022 nan 0.0100 0.0042
## 3 1.2925 nan 0.0100 0.0046
## 4 1.2834 nan 0.0100 0.0040
## 5 1.2746 nan 0.0100 0.0040
## 6 1.2655 nan 0.0100 0.0040
## 7 1.2571 nan 0.0100 0.0038
## 8 1.2488 nan 0.0100 0.0035
## 9 1.2408 nan 0.0100 0.0040
## 10 1.2332 nan 0.0100 0.0035
## 20 1.1613 nan 0.0100 0.0028
## 40 1.0422 nan 0.0100 0.0022
## 60 0.9529 nan 0.0100 0.0017
## 80 0.8836 nan 0.0100 0.0010
## 100 0.8282 nan 0.0100 0.0009
## 120 0.7836 nan 0.0100 0.0005
## 140 0.7467 nan 0.0100 0.0005
## 160 0.7130 nan 0.0100 0.0004
## 180 0.6845 nan 0.0100 0.0006
## 200 0.6595 nan 0.0100 0.0003
## 220 0.6374 nan 0.0100 0.0000
## 240 0.6183 nan 0.0100 0.0003
## 260 0.6004 nan 0.0100 0.0001
## 280 0.5832 nan 0.0100 0.0001
## 300 0.5660 nan 0.0100 -0.0000
## 320 0.5519 nan 0.0100 -0.0001
## 340 0.5378 nan 0.0100 0.0001
## 360 0.5252 nan 0.0100 -0.0000
## 380 0.5128 nan 0.0100 0.0000
## 400 0.5011 nan 0.0100 -0.0001
## 420 0.4898 nan 0.0100 -0.0001
## 440 0.4797 nan 0.0100 -0.0000
## 460 0.4693 nan 0.0100 0.0000
## 480 0.4590 nan 0.0100 -0.0001
## 500 0.4486 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0394
## 2 1.1697 nan 0.1000 0.0295
## 3 1.1135 nan 0.1000 0.0238
## 4 1.0599 nan 0.1000 0.0250
## 5 1.0173 nan 0.1000 0.0202
## 6 0.9851 nan 0.1000 0.0122
## 7 0.9488 nan 0.1000 0.0130
## 8 0.9203 nan 0.1000 0.0122
## 9 0.8975 nan 0.1000 0.0073
## 10 0.8697 nan 0.1000 0.0102
## 20 0.7106 nan 0.1000 0.0014
## 40 0.5850 nan 0.1000 -0.0010
## 60 0.5100 nan 0.1000 -0.0005
## 80 0.4385 nan 0.1000 0.0015
## 100 0.3836 nan 0.1000 0.0001
## 120 0.3477 nan 0.1000 -0.0007
## 140 0.3090 nan 0.1000 -0.0007
## 160 0.2765 nan 0.1000 -0.0000
## 180 0.2488 nan 0.1000 0.0001
## 200 0.2228 nan 0.1000 -0.0005
## 220 0.2028 nan 0.1000 -0.0005
## 240 0.1860 nan 0.1000 -0.0006
## 260 0.1689 nan 0.1000 -0.0001
## 280 0.1536 nan 0.1000 -0.0004
## 300 0.1408 nan 0.1000 -0.0003
## 320 0.1276 nan 0.1000 -0.0006
## 340 0.1163 nan 0.1000 -0.0004
## 360 0.1053 nan 0.1000 -0.0005
## 380 0.0961 nan 0.1000 -0.0001
## 400 0.0878 nan 0.1000 -0.0002
## 420 0.0813 nan 0.1000 -0.0001
## 440 0.0745 nan 0.1000 -0.0001
## 460 0.0690 nan 0.1000 -0.0000
## 480 0.0641 nan 0.1000 -0.0002
## 500 0.0589 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2380 nan 0.1000 0.0384
## 2 1.1715 nan 0.1000 0.0311
## 3 1.1129 nan 0.1000 0.0250
## 4 1.0559 nan 0.1000 0.0241
## 5 1.0116 nan 0.1000 0.0165
## 6 0.9746 nan 0.1000 0.0150
## 7 0.9405 nan 0.1000 0.0130
## 8 0.9096 nan 0.1000 0.0122
## 9 0.8853 nan 0.1000 0.0095
## 10 0.8607 nan 0.1000 0.0081
## 20 0.7045 nan 0.1000 0.0032
## 40 0.5829 nan 0.1000 -0.0017
## 60 0.5000 nan 0.1000 0.0013
## 80 0.4396 nan 0.1000 -0.0015
## 100 0.3911 nan 0.1000 -0.0005
## 120 0.3465 nan 0.1000 -0.0007
## 140 0.3068 nan 0.1000 -0.0001
## 160 0.2746 nan 0.1000 -0.0012
## 180 0.2480 nan 0.1000 -0.0006
## 200 0.2248 nan 0.1000 -0.0008
## 220 0.2033 nan 0.1000 0.0001
## 240 0.1825 nan 0.1000 -0.0004
## 260 0.1661 nan 0.1000 -0.0006
## 280 0.1524 nan 0.1000 -0.0001
## 300 0.1402 nan 0.1000 -0.0004
## 320 0.1280 nan 0.1000 -0.0004
## 340 0.1176 nan 0.1000 -0.0005
## 360 0.1078 nan 0.1000 -0.0002
## 380 0.0980 nan 0.1000 -0.0002
## 400 0.0902 nan 0.1000 -0.0003
## 420 0.0837 nan 0.1000 -0.0002
## 440 0.0771 nan 0.1000 -0.0004
## 460 0.0714 nan 0.1000 -0.0002
## 480 0.0658 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2515 nan 0.1000 0.0322
## 2 1.1786 nan 0.1000 0.0322
## 3 1.1205 nan 0.1000 0.0236
## 4 1.0700 nan 0.1000 0.0204
## 5 1.0232 nan 0.1000 0.0195
## 6 0.9869 nan 0.1000 0.0160
## 7 0.9537 nan 0.1000 0.0137
## 8 0.9200 nan 0.1000 0.0125
## 9 0.8963 nan 0.1000 0.0114
## 10 0.8733 nan 0.1000 0.0080
## 20 0.7202 nan 0.1000 0.0021
## 40 0.5902 nan 0.1000 -0.0005
## 60 0.5128 nan 0.1000 -0.0012
## 80 0.4548 nan 0.1000 0.0004
## 100 0.3988 nan 0.1000 0.0001
## 120 0.3571 nan 0.1000 0.0002
## 140 0.3222 nan 0.1000 0.0003
## 160 0.2921 nan 0.1000 -0.0000
## 180 0.2610 nan 0.1000 -0.0008
## 200 0.2376 nan 0.1000 -0.0013
## 220 0.2164 nan 0.1000 -0.0010
## 240 0.1966 nan 0.1000 -0.0009
## 260 0.1782 nan 0.1000 -0.0004
## 280 0.1612 nan 0.1000 -0.0007
## 300 0.1488 nan 0.1000 -0.0001
## 320 0.1361 nan 0.1000 -0.0006
## 340 0.1256 nan 0.1000 -0.0007
## 360 0.1149 nan 0.1000 -0.0001
## 380 0.1053 nan 0.1000 -0.0003
## 400 0.0971 nan 0.1000 -0.0004
## 420 0.0892 nan 0.1000 -0.0004
## 440 0.0819 nan 0.1000 -0.0004
## 460 0.0766 nan 0.1000 -0.0004
## 480 0.0707 nan 0.1000 -0.0002
## 500 0.0654 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2283 nan 0.1000 0.0425
## 2 1.1602 nan 0.1000 0.0324
## 3 1.0988 nan 0.1000 0.0244
## 4 1.0405 nan 0.1000 0.0269
## 5 0.9900 nan 0.1000 0.0153
## 6 0.9509 nan 0.1000 0.0168
## 7 0.9192 nan 0.1000 0.0110
## 8 0.8927 nan 0.1000 0.0091
## 9 0.8634 nan 0.1000 0.0122
## 10 0.8385 nan 0.1000 0.0071
## 20 0.6704 nan 0.1000 0.0038
## 40 0.5280 nan 0.1000 -0.0016
## 60 0.4374 nan 0.1000 0.0000
## 80 0.3716 nan 0.1000 -0.0009
## 100 0.3165 nan 0.1000 -0.0004
## 120 0.2692 nan 0.1000 -0.0018
## 140 0.2315 nan 0.1000 -0.0001
## 160 0.1996 nan 0.1000 -0.0006
## 180 0.1756 nan 0.1000 -0.0003
## 200 0.1527 nan 0.1000 -0.0003
## 220 0.1356 nan 0.1000 -0.0003
## 240 0.1205 nan 0.1000 -0.0000
## 260 0.1084 nan 0.1000 -0.0002
## 280 0.0972 nan 0.1000 -0.0002
## 300 0.0874 nan 0.1000 -0.0001
## 320 0.0784 nan 0.1000 -0.0002
## 340 0.0702 nan 0.1000 -0.0001
## 360 0.0632 nan 0.1000 -0.0001
## 380 0.0566 nan 0.1000 -0.0001
## 400 0.0501 nan 0.1000 -0.0001
## 420 0.0451 nan 0.1000 -0.0001
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## 460 0.0373 nan 0.1000 -0.0001
## 480 0.0334 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.1000 0.0440
## 2 1.1548 nan 0.1000 0.0302
## 3 1.0927 nan 0.1000 0.0259
## 4 1.0366 nan 0.1000 0.0212
## 5 0.9925 nan 0.1000 0.0195
## 6 0.9550 nan 0.1000 0.0171
## 7 0.9190 nan 0.1000 0.0155
## 8 0.8867 nan 0.1000 0.0116
## 9 0.8576 nan 0.1000 0.0110
## 10 0.8326 nan 0.1000 0.0090
## 20 0.6773 nan 0.1000 0.0026
## 40 0.5369 nan 0.1000 -0.0012
## 60 0.4528 nan 0.1000 0.0000
## 80 0.3841 nan 0.1000 -0.0008
## 100 0.3242 nan 0.1000 0.0004
## 120 0.2822 nan 0.1000 -0.0017
## 140 0.2462 nan 0.1000 -0.0009
## 160 0.2162 nan 0.1000 -0.0005
## 180 0.1890 nan 0.1000 -0.0004
## 200 0.1653 nan 0.1000 -0.0001
## 220 0.1458 nan 0.1000 -0.0007
## 240 0.1283 nan 0.1000 0.0001
## 260 0.1149 nan 0.1000 -0.0004
## 280 0.1028 nan 0.1000 -0.0003
## 300 0.0933 nan 0.1000 -0.0005
## 320 0.0833 nan 0.1000 -0.0003
## 340 0.0751 nan 0.1000 -0.0002
## 360 0.0663 nan 0.1000 -0.0002
## 380 0.0592 nan 0.1000 -0.0002
## 400 0.0533 nan 0.1000 -0.0001
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## 440 0.0439 nan 0.1000 -0.0003
## 460 0.0391 nan 0.1000 -0.0002
## 480 0.0349 nan 0.1000 -0.0001
## 500 0.0315 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0395
## 2 1.1588 nan 0.1000 0.0318
## 3 1.0987 nan 0.1000 0.0264
## 4 1.0490 nan 0.1000 0.0215
## 5 1.0089 nan 0.1000 0.0173
## 6 0.9698 nan 0.1000 0.0162
## 7 0.9311 nan 0.1000 0.0158
## 8 0.8981 nan 0.1000 0.0137
## 9 0.8698 nan 0.1000 0.0097
## 10 0.8453 nan 0.1000 0.0102
## 20 0.6898 nan 0.1000 0.0014
## 40 0.5395 nan 0.1000 0.0004
## 60 0.4526 nan 0.1000 -0.0000
## 80 0.3807 nan 0.1000 -0.0008
## 100 0.3286 nan 0.1000 -0.0007
## 120 0.2910 nan 0.1000 -0.0001
## 140 0.2515 nan 0.1000 -0.0006
## 160 0.2217 nan 0.1000 -0.0002
## 180 0.1971 nan 0.1000 -0.0009
## 200 0.1737 nan 0.1000 -0.0007
## 220 0.1535 nan 0.1000 -0.0007
## 240 0.1359 nan 0.1000 -0.0010
## 260 0.1219 nan 0.1000 -0.0006
## 280 0.1080 nan 0.1000 -0.0003
## 300 0.0981 nan 0.1000 -0.0005
## 320 0.0890 nan 0.1000 -0.0004
## 340 0.0806 nan 0.1000 -0.0002
## 360 0.0730 nan 0.1000 -0.0001
## 380 0.0660 nan 0.1000 -0.0003
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## 460 0.0432 nan 0.1000 -0.0001
## 480 0.0389 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2234 nan 0.1000 0.0478
## 2 1.1554 nan 0.1000 0.0303
## 3 1.0933 nan 0.1000 0.0298
## 4 1.0376 nan 0.1000 0.0242
## 5 0.9835 nan 0.1000 0.0229
## 6 0.9388 nan 0.1000 0.0189
## 7 0.9036 nan 0.1000 0.0141
## 8 0.8730 nan 0.1000 0.0102
## 9 0.8409 nan 0.1000 0.0132
## 10 0.8129 nan 0.1000 0.0094
## 20 0.6465 nan 0.1000 0.0030
## 40 0.4794 nan 0.1000 0.0004
## 60 0.3904 nan 0.1000 -0.0010
## 80 0.3202 nan 0.1000 -0.0004
## 100 0.2656 nan 0.1000 -0.0003
## 120 0.2211 nan 0.1000 -0.0008
## 140 0.1881 nan 0.1000 -0.0004
## 160 0.1634 nan 0.1000 -0.0003
## 180 0.1420 nan 0.1000 -0.0005
## 200 0.1209 nan 0.1000 -0.0000
## 220 0.1031 nan 0.1000 -0.0001
## 240 0.0889 nan 0.1000 -0.0000
## 260 0.0766 nan 0.1000 -0.0002
## 280 0.0677 nan 0.1000 -0.0002
## 300 0.0592 nan 0.1000 -0.0004
## 320 0.0524 nan 0.1000 -0.0001
## 340 0.0452 nan 0.1000 -0.0003
## 360 0.0399 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2292 nan 0.1000 0.0408
## 2 1.1470 nan 0.1000 0.0381
## 3 1.0858 nan 0.1000 0.0260
## 4 1.0336 nan 0.1000 0.0230
## 5 0.9890 nan 0.1000 0.0180
## 6 0.9505 nan 0.1000 0.0156
## 7 0.9115 nan 0.1000 0.0129
## 8 0.8763 nan 0.1000 0.0139
## 9 0.8489 nan 0.1000 0.0099
## 10 0.8214 nan 0.1000 0.0110
## 20 0.6611 nan 0.1000 0.0029
## 40 0.5005 nan 0.1000 -0.0016
## 60 0.4032 nan 0.1000 -0.0013
## 80 0.3344 nan 0.1000 -0.0011
## 100 0.2879 nan 0.1000 -0.0003
## 120 0.2415 nan 0.1000 -0.0002
## 140 0.2060 nan 0.1000 0.0001
## 160 0.1745 nan 0.1000 -0.0008
## 180 0.1481 nan 0.1000 -0.0008
## 200 0.1249 nan 0.1000 -0.0000
## 220 0.1074 nan 0.1000 -0.0000
## 240 0.0925 nan 0.1000 -0.0003
## 260 0.0790 nan 0.1000 -0.0002
## 280 0.0690 nan 0.1000 0.0000
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## 320 0.0518 nan 0.1000 -0.0000
## 340 0.0458 nan 0.1000 -0.0003
## 360 0.0407 nan 0.1000 -0.0002
## 380 0.0353 nan 0.1000 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0367
## 2 1.1629 nan 0.1000 0.0351
## 3 1.1008 nan 0.1000 0.0252
## 4 1.0391 nan 0.1000 0.0281
## 5 0.9928 nan 0.1000 0.0202
## 6 0.9499 nan 0.1000 0.0188
## 7 0.9065 nan 0.1000 0.0157
## 8 0.8758 nan 0.1000 0.0106
## 9 0.8465 nan 0.1000 0.0106
## 10 0.8163 nan 0.1000 0.0122
## 20 0.6473 nan 0.1000 0.0032
## 40 0.4988 nan 0.1000 0.0005
## 60 0.4048 nan 0.1000 0.0001
## 80 0.3357 nan 0.1000 -0.0006
## 100 0.2834 nan 0.1000 -0.0001
## 120 0.2416 nan 0.1000 -0.0007
## 140 0.2043 nan 0.1000 -0.0006
## 160 0.1766 nan 0.1000 -0.0011
## 180 0.1526 nan 0.1000 -0.0007
## 200 0.1318 nan 0.1000 -0.0002
## 220 0.1145 nan 0.1000 -0.0005
## 240 0.1003 nan 0.1000 -0.0005
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## 280 0.0765 nan 0.1000 -0.0001
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## 320 0.0594 nan 0.1000 -0.0003
## 340 0.0525 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2537 nan 0.0010 0.0003
## 100 1.2381 nan 0.0010 0.0003
## 120 1.2229 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1808 nan 0.0010 0.0003
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## 300 1.1069 nan 0.0010 0.0002
## 320 1.0957 nan 0.0010 0.0002
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## 380 1.0639 nan 0.0010 0.0002
## 400 1.0538 nan 0.0010 0.0002
## 420 1.0441 nan 0.0010 0.0002
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## 460 1.0256 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2539 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2091 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0004
## 180 1.1815 nan 0.0010 0.0003
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## 360 1.0752 nan 0.0010 0.0002
## 380 1.0651 nan 0.0010 0.0002
## 400 1.0552 nan 0.0010 0.0002
## 420 1.0456 nan 0.0010 0.0002
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## 460 1.0269 nan 0.0010 0.0002
## 480 1.0181 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2536 nan 0.0010 0.0003
## 100 1.2380 nan 0.0010 0.0004
## 120 1.2231 nan 0.0010 0.0003
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1948 nan 0.0010 0.0003
## 180 1.1814 nan 0.0010 0.0003
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## 240 1.1436 nan 0.0010 0.0002
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## 380 1.0664 nan 0.0010 0.0002
## 400 1.0567 nan 0.0010 0.0002
## 420 1.0470 nan 0.0010 0.0002
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## 460 1.0288 nan 0.0010 0.0002
## 480 1.0202 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0005
## 60 1.2661 nan 0.0010 0.0004
## 80 1.2489 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0004
## 120 1.2165 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
## 160 1.1855 nan 0.0010 0.0004
## 180 1.1709 nan 0.0010 0.0004
## 200 1.1570 nan 0.0010 0.0003
## 220 1.1432 nan 0.0010 0.0003
## 240 1.1301 nan 0.0010 0.0002
## 260 1.1173 nan 0.0010 0.0003
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## 340 1.0702 nan 0.0010 0.0002
## 360 1.0592 nan 0.0010 0.0002
## 380 1.0487 nan 0.0010 0.0002
## 400 1.0384 nan 0.0010 0.0002
## 420 1.0283 nan 0.0010 0.0002
## 440 1.0182 nan 0.0010 0.0002
## 460 1.0088 nan 0.0010 0.0002
## 480 0.9997 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2660 nan 0.0010 0.0004
## 80 1.2490 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0004
## 120 1.2163 nan 0.0010 0.0004
## 140 1.2012 nan 0.0010 0.0003
## 160 1.1860 nan 0.0010 0.0004
## 180 1.1713 nan 0.0010 0.0003
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## 220 1.1439 nan 0.0010 0.0003
## 240 1.1310 nan 0.0010 0.0003
## 260 1.1182 nan 0.0010 0.0002
## 280 1.1056 nan 0.0010 0.0003
## 300 1.0934 nan 0.0010 0.0003
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## 340 1.0707 nan 0.0010 0.0002
## 360 1.0596 nan 0.0010 0.0003
## 380 1.0490 nan 0.0010 0.0002
## 400 1.0385 nan 0.0010 0.0002
## 420 1.0283 nan 0.0010 0.0002
## 440 1.0186 nan 0.0010 0.0002
## 460 1.0090 nan 0.0010 0.0002
## 480 0.9995 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0004
## 120 1.2179 nan 0.0010 0.0003
## 140 1.2028 nan 0.0010 0.0004
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0002
## 200 1.1598 nan 0.0010 0.0003
## 220 1.1459 nan 0.0010 0.0004
## 240 1.1328 nan 0.0010 0.0003
## 260 1.1197 nan 0.0010 0.0003
## 280 1.1074 nan 0.0010 0.0003
## 300 1.0955 nan 0.0010 0.0003
## 320 1.0837 nan 0.0010 0.0003
## 340 1.0724 nan 0.0010 0.0002
## 360 1.0614 nan 0.0010 0.0003
## 380 1.0507 nan 0.0010 0.0003
## 400 1.0403 nan 0.0010 0.0002
## 420 1.0304 nan 0.0010 0.0002
## 440 1.0207 nan 0.0010 0.0002
## 460 1.0112 nan 0.0010 0.0002
## 480 1.0019 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0005
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3127 nan 0.0010 0.0005
## 9 1.3117 nan 0.0010 0.0004
## 10 1.3106 nan 0.0010 0.0005
## 20 1.3009 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2636 nan 0.0010 0.0004
## 80 1.2455 nan 0.0010 0.0004
## 100 1.2283 nan 0.0010 0.0004
## 120 1.2117 nan 0.0010 0.0004
## 140 1.1956 nan 0.0010 0.0004
## 160 1.1796 nan 0.0010 0.0003
## 180 1.1644 nan 0.0010 0.0003
## 200 1.1499 nan 0.0010 0.0003
## 220 1.1357 nan 0.0010 0.0003
## 240 1.1217 nan 0.0010 0.0003
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## 280 1.0955 nan 0.0010 0.0003
## 300 1.0831 nan 0.0010 0.0003
## 320 1.0708 nan 0.0010 0.0003
## 340 1.0591 nan 0.0010 0.0002
## 360 1.0475 nan 0.0010 0.0003
## 380 1.0366 nan 0.0010 0.0003
## 400 1.0259 nan 0.0010 0.0002
## 420 1.0152 nan 0.0010 0.0003
## 440 1.0048 nan 0.0010 0.0002
## 460 0.9949 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3112 nan 0.0010 0.0005
## 20 1.3013 nan 0.0010 0.0005
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2640 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0003
## 100 1.2291 nan 0.0010 0.0004
## 120 1.2125 nan 0.0010 0.0004
## 140 1.1963 nan 0.0010 0.0004
## 160 1.1807 nan 0.0010 0.0003
## 180 1.1656 nan 0.0010 0.0003
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## 220 1.1366 nan 0.0010 0.0003
## 240 1.1228 nan 0.0010 0.0003
## 260 1.1097 nan 0.0010 0.0003
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## 300 1.0842 nan 0.0010 0.0003
## 320 1.0718 nan 0.0010 0.0003
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## 360 1.0486 nan 0.0010 0.0002
## 380 1.0374 nan 0.0010 0.0002
## 400 1.0269 nan 0.0010 0.0002
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## 460 0.9963 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2639 nan 0.0010 0.0004
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## 120 1.2134 nan 0.0010 0.0004
## 140 1.1977 nan 0.0010 0.0004
## 160 1.1826 nan 0.0010 0.0003
## 180 1.1678 nan 0.0010 0.0003
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## 280 1.0997 nan 0.0010 0.0003
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## 320 1.0752 nan 0.0010 0.0003
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## 360 1.0518 nan 0.0010 0.0002
## 380 1.0406 nan 0.0010 0.0002
## 400 1.0301 nan 0.0010 0.0002
## 420 1.0198 nan 0.0010 0.0002
## 440 1.0096 nan 0.0010 0.0002
## 460 0.9999 nan 0.0010 0.0002
## 480 0.9903 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3039 nan 0.0100 0.0036
## 3 1.2944 nan 0.0100 0.0039
## 4 1.2847 nan 0.0100 0.0040
## 5 1.2764 nan 0.0100 0.0039
## 6 1.2685 nan 0.0100 0.0037
## 7 1.2596 nan 0.0100 0.0039
## 8 1.2512 nan 0.0100 0.0035
## 9 1.2435 nan 0.0100 0.0037
## 10 1.2357 nan 0.0100 0.0034
## 20 1.1639 nan 0.0100 0.0030
## 40 1.0546 nan 0.0100 0.0020
## 60 0.9689 nan 0.0100 0.0012
## 80 0.9002 nan 0.0100 0.0013
## 100 0.8466 nan 0.0100 0.0010
## 120 0.8034 nan 0.0100 0.0008
## 140 0.7664 nan 0.0100 0.0005
## 160 0.7357 nan 0.0100 0.0002
## 180 0.7087 nan 0.0100 0.0002
## 200 0.6873 nan 0.0100 0.0001
## 220 0.6669 nan 0.0100 0.0000
## 240 0.6491 nan 0.0100 0.0001
## 260 0.6333 nan 0.0100 0.0001
## 280 0.6196 nan 0.0100 0.0002
## 300 0.6075 nan 0.0100 -0.0000
## 320 0.5956 nan 0.0100 -0.0000
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## 380 0.5641 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0042
## 2 1.3035 nan 0.0100 0.0044
## 3 1.2953 nan 0.0100 0.0038
## 4 1.2862 nan 0.0100 0.0040
## 5 1.2776 nan 0.0100 0.0038
## 6 1.2698 nan 0.0100 0.0036
## 7 1.2620 nan 0.0100 0.0032
## 8 1.2535 nan 0.0100 0.0039
## 9 1.2457 nan 0.0100 0.0040
## 10 1.2384 nan 0.0100 0.0037
## 20 1.1690 nan 0.0100 0.0027
## 40 1.0570 nan 0.0100 0.0022
## 60 0.9708 nan 0.0100 0.0017
## 80 0.9043 nan 0.0100 0.0014
## 100 0.8499 nan 0.0100 0.0010
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## 140 0.7686 nan 0.0100 0.0006
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## 180 0.7126 nan 0.0100 0.0004
## 200 0.6909 nan 0.0100 0.0003
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## 240 0.6551 nan 0.0100 -0.0001
## 260 0.6398 nan 0.0100 0.0002
## 280 0.6277 nan 0.0100 0.0001
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## 320 0.6023 nan 0.0100 -0.0001
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## 380 0.5703 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3038 nan 0.0100 0.0039
## 3 1.2953 nan 0.0100 0.0041
## 4 1.2863 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0037
## 6 1.2704 nan 0.0100 0.0033
## 7 1.2622 nan 0.0100 0.0036
## 8 1.2539 nan 0.0100 0.0039
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## 20 1.1698 nan 0.0100 0.0029
## 40 1.0564 nan 0.0100 0.0019
## 60 0.9706 nan 0.0100 0.0016
## 80 0.9017 nan 0.0100 0.0008
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## 140 0.7704 nan 0.0100 0.0003
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## 240 0.6604 nan 0.0100 0.0001
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## 280 0.6327 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2928 nan 0.0100 0.0041
## 4 1.2843 nan 0.0100 0.0038
## 5 1.2752 nan 0.0100 0.0043
## 6 1.2663 nan 0.0100 0.0043
## 7 1.2577 nan 0.0100 0.0038
## 8 1.2494 nan 0.0100 0.0039
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## 40 1.0388 nan 0.0100 0.0023
## 60 0.9495 nan 0.0100 0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2926 nan 0.0100 0.0042
## 4 1.2834 nan 0.0100 0.0043
## 5 1.2747 nan 0.0100 0.0040
## 6 1.2660 nan 0.0100 0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0049
## 2 1.3023 nan 0.0100 0.0046
## 3 1.2936 nan 0.0100 0.0043
## 4 1.2852 nan 0.0100 0.0038
## 5 1.2762 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0036
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## 20 1.1608 nan 0.0100 0.0031
## 40 1.0416 nan 0.0100 0.0023
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0048
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2915 nan 0.0100 0.0047
## 4 1.2813 nan 0.0100 0.0046
## 5 1.2729 nan 0.0100 0.0037
## 6 1.2636 nan 0.0100 0.0042
## 7 1.2543 nan 0.0100 0.0041
## 8 1.2456 nan 0.0100 0.0035
## 9 1.2373 nan 0.0100 0.0041
## 10 1.2286 nan 0.0100 0.0036
## 20 1.1510 nan 0.0100 0.0036
## 40 1.0260 nan 0.0100 0.0025
## 60 0.9328 nan 0.0100 0.0018
## 80 0.8596 nan 0.0100 0.0011
## 100 0.8009 nan 0.0100 0.0012
## 120 0.7524 nan 0.0100 0.0009
## 140 0.7122 nan 0.0100 0.0006
## 160 0.6782 nan 0.0100 0.0004
## 180 0.6505 nan 0.0100 0.0003
## 200 0.6253 nan 0.0100 0.0003
## 220 0.6027 nan 0.0100 0.0001
## 240 0.5823 nan 0.0100 0.0001
## 260 0.5650 nan 0.0100 0.0001
## 280 0.5485 nan 0.0100 0.0001
## 300 0.5331 nan 0.0100 0.0000
## 320 0.5193 nan 0.0100 -0.0000
## 340 0.5078 nan 0.0100 -0.0001
## 360 0.4945 nan 0.0100 0.0000
## 380 0.4830 nan 0.0100 0.0001
## 400 0.4710 nan 0.0100 0.0000
## 420 0.4609 nan 0.0100 0.0001
## 440 0.4509 nan 0.0100 0.0000
## 460 0.4417 nan 0.0100 -0.0002
## 480 0.4323 nan 0.0100 -0.0000
## 500 0.4235 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0048
## 2 1.3004 nan 0.0100 0.0048
## 3 1.2908 nan 0.0100 0.0044
## 4 1.2817 nan 0.0100 0.0038
## 5 1.2719 nan 0.0100 0.0044
## 6 1.2626 nan 0.0100 0.0040
## 7 1.2537 nan 0.0100 0.0038
## 8 1.2442 nan 0.0100 0.0045
## 9 1.2359 nan 0.0100 0.0034
## 10 1.2273 nan 0.0100 0.0039
## 20 1.1502 nan 0.0100 0.0033
## 40 1.0278 nan 0.0100 0.0023
## 60 0.9350 nan 0.0100 0.0016
## 80 0.8607 nan 0.0100 0.0011
## 100 0.8036 nan 0.0100 0.0007
## 120 0.7573 nan 0.0100 0.0005
## 140 0.7182 nan 0.0100 0.0004
## 160 0.6840 nan 0.0100 0.0005
## 180 0.6555 nan 0.0100 0.0003
## 200 0.6328 nan 0.0100 0.0001
## 220 0.6110 nan 0.0100 0.0002
## 240 0.5920 nan 0.0100 0.0003
## 260 0.5750 nan 0.0100 -0.0000
## 280 0.5587 nan 0.0100 0.0001
## 300 0.5443 nan 0.0100 0.0001
## 320 0.5313 nan 0.0100 -0.0000
## 340 0.5184 nan 0.0100 -0.0000
## 360 0.5062 nan 0.0100 -0.0001
## 380 0.4946 nan 0.0100 -0.0001
## 400 0.4833 nan 0.0100 -0.0001
## 420 0.4737 nan 0.0100 -0.0001
## 440 0.4632 nan 0.0100 -0.0001
## 460 0.4531 nan 0.0100 0.0000
## 480 0.4434 nan 0.0100 -0.0000
## 500 0.4344 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0046
## 2 1.3010 nan 0.0100 0.0050
## 3 1.2909 nan 0.0100 0.0049
## 4 1.2817 nan 0.0100 0.0044
## 5 1.2724 nan 0.0100 0.0045
## 6 1.2638 nan 0.0100 0.0039
## 7 1.2552 nan 0.0100 0.0040
## 8 1.2464 nan 0.0100 0.0038
## 9 1.2373 nan 0.0100 0.0039
## 10 1.2293 nan 0.0100 0.0035
## 20 1.1525 nan 0.0100 0.0029
## 40 1.0303 nan 0.0100 0.0026
## 60 0.9389 nan 0.0100 0.0018
## 80 0.8655 nan 0.0100 0.0014
## 100 0.8087 nan 0.0100 0.0008
## 120 0.7616 nan 0.0100 0.0005
## 140 0.7246 nan 0.0100 0.0005
## 160 0.6919 nan 0.0100 0.0004
## 180 0.6636 nan 0.0100 0.0002
## 200 0.6400 nan 0.0100 0.0000
## 220 0.6190 nan 0.0100 0.0000
## 240 0.6005 nan 0.0100 0.0000
## 260 0.5842 nan 0.0100 0.0002
## 280 0.5689 nan 0.0100 -0.0000
## 300 0.5543 nan 0.0100 0.0001
## 320 0.5406 nan 0.0100 -0.0000
## 340 0.5278 nan 0.0100 0.0000
## 360 0.5156 nan 0.0100 -0.0000
## 380 0.5044 nan 0.0100 0.0001
## 400 0.4935 nan 0.0100 -0.0000
## 420 0.4833 nan 0.0100 0.0001
## 440 0.4739 nan 0.0100 -0.0001
## 460 0.4645 nan 0.0100 -0.0001
## 480 0.4555 nan 0.0100 -0.0001
## 500 0.4460 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2313 nan 0.1000 0.0401
## 2 1.1619 nan 0.1000 0.0303
## 3 1.0991 nan 0.1000 0.0261
## 4 1.0463 nan 0.1000 0.0204
## 5 0.9993 nan 0.1000 0.0211
## 6 0.9610 nan 0.1000 0.0163
## 7 0.9249 nan 0.1000 0.0145
## 8 0.8918 nan 0.1000 0.0129
## 9 0.8676 nan 0.1000 0.0092
## 10 0.8430 nan 0.1000 0.0098
## 20 0.6930 nan 0.1000 0.0014
## 40 0.5634 nan 0.1000 0.0010
## 60 0.4858 nan 0.1000 -0.0007
## 80 0.4347 nan 0.1000 -0.0014
## 100 0.3855 nan 0.1000 -0.0001
## 120 0.3451 nan 0.1000 -0.0006
## 140 0.3055 nan 0.1000 -0.0008
## 160 0.2741 nan 0.1000 -0.0005
## 180 0.2461 nan 0.1000 -0.0008
## 200 0.2197 nan 0.1000 -0.0006
## 220 0.2019 nan 0.1000 -0.0005
## 240 0.1833 nan 0.1000 -0.0002
## 260 0.1677 nan 0.1000 0.0000
## 280 0.1530 nan 0.1000 -0.0003
## 300 0.1392 nan 0.1000 -0.0003
## 320 0.1257 nan 0.1000 -0.0003
## 340 0.1143 nan 0.1000 -0.0004
## 360 0.1047 nan 0.1000 -0.0002
## 380 0.0967 nan 0.1000 -0.0004
## 400 0.0882 nan 0.1000 -0.0003
## 420 0.0815 nan 0.1000 0.0000
## 440 0.0752 nan 0.1000 -0.0002
## 460 0.0695 nan 0.1000 -0.0002
## 480 0.0648 nan 0.1000 -0.0000
## 500 0.0597 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0386
## 2 1.1649 nan 0.1000 0.0358
## 3 1.1048 nan 0.1000 0.0283
## 4 1.0486 nan 0.1000 0.0241
## 5 1.0096 nan 0.1000 0.0159
## 6 0.9680 nan 0.1000 0.0189
## 7 0.9273 nan 0.1000 0.0176
## 8 0.8950 nan 0.1000 0.0132
## 9 0.8638 nan 0.1000 0.0124
## 10 0.8386 nan 0.1000 0.0096
## 20 0.6892 nan 0.1000 0.0018
## 40 0.5648 nan 0.1000 0.0002
## 60 0.4874 nan 0.1000 -0.0011
## 80 0.4313 nan 0.1000 0.0003
## 100 0.3890 nan 0.1000 -0.0009
## 120 0.3490 nan 0.1000 -0.0004
## 140 0.3119 nan 0.1000 -0.0010
## 160 0.2829 nan 0.1000 -0.0002
## 180 0.2567 nan 0.1000 -0.0006
## 200 0.2349 nan 0.1000 -0.0007
## 220 0.2123 nan 0.1000 -0.0006
## 240 0.1913 nan 0.1000 -0.0007
## 260 0.1736 nan 0.1000 -0.0004
## 280 0.1562 nan 0.1000 -0.0003
## 300 0.1420 nan 0.1000 -0.0002
## 320 0.1313 nan 0.1000 -0.0005
## 340 0.1197 nan 0.1000 -0.0004
## 360 0.1094 nan 0.1000 -0.0004
## 380 0.1006 nan 0.1000 -0.0006
## 400 0.0932 nan 0.1000 -0.0003
## 420 0.0861 nan 0.1000 -0.0002
## 440 0.0802 nan 0.1000 -0.0004
## 460 0.0736 nan 0.1000 -0.0002
## 480 0.0672 nan 0.1000 -0.0001
## 500 0.0620 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2350 nan 0.1000 0.0416
## 2 1.1605 nan 0.1000 0.0337
## 3 1.1020 nan 0.1000 0.0249
## 4 1.0483 nan 0.1000 0.0224
## 5 1.0019 nan 0.1000 0.0205
## 6 0.9647 nan 0.1000 0.0137
## 7 0.9349 nan 0.1000 0.0114
## 8 0.9046 nan 0.1000 0.0129
## 9 0.8776 nan 0.1000 0.0115
## 10 0.8534 nan 0.1000 0.0081
## 20 0.7043 nan 0.1000 -0.0003
## 40 0.5770 nan 0.1000 -0.0001
## 60 0.5032 nan 0.1000 -0.0004
## 80 0.4435 nan 0.1000 -0.0004
## 100 0.3954 nan 0.1000 -0.0006
## 120 0.3573 nan 0.1000 -0.0019
## 140 0.3235 nan 0.1000 -0.0014
## 160 0.2912 nan 0.1000 -0.0004
## 180 0.2658 nan 0.1000 -0.0006
## 200 0.2428 nan 0.1000 -0.0003
## 220 0.2251 nan 0.1000 -0.0007
## 240 0.2013 nan 0.1000 -0.0010
## 260 0.1834 nan 0.1000 -0.0001
## 280 0.1680 nan 0.1000 -0.0005
## 300 0.1540 nan 0.1000 -0.0004
## 320 0.1423 nan 0.1000 -0.0004
## 340 0.1336 nan 0.1000 -0.0002
## 360 0.1246 nan 0.1000 -0.0003
## 380 0.1165 nan 0.1000 -0.0002
## 400 0.1073 nan 0.1000 -0.0003
## 420 0.0994 nan 0.1000 -0.0003
## 440 0.0916 nan 0.1000 -0.0003
## 460 0.0837 nan 0.1000 -0.0004
## 480 0.0780 nan 0.1000 -0.0002
## 500 0.0720 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2300 nan 0.1000 0.0410
## 2 1.1520 nan 0.1000 0.0382
## 3 1.0896 nan 0.1000 0.0251
## 4 1.0334 nan 0.1000 0.0255
## 5 0.9906 nan 0.1000 0.0166
## 6 0.9451 nan 0.1000 0.0201
## 7 0.9070 nan 0.1000 0.0138
## 8 0.8789 nan 0.1000 0.0122
## 9 0.8444 nan 0.1000 0.0130
## 10 0.8204 nan 0.1000 0.0064
## 20 0.6635 nan 0.1000 0.0041
## 40 0.5267 nan 0.1000 0.0005
## 60 0.4430 nan 0.1000 -0.0018
## 80 0.3726 nan 0.1000 -0.0002
## 100 0.3246 nan 0.1000 0.0003
## 120 0.2777 nan 0.1000 -0.0002
## 140 0.2384 nan 0.1000 0.0002
## 160 0.2064 nan 0.1000 -0.0001
## 180 0.1840 nan 0.1000 -0.0006
## 200 0.1648 nan 0.1000 -0.0002
## 220 0.1446 nan 0.1000 -0.0002
## 240 0.1270 nan 0.1000 -0.0000
## 260 0.1122 nan 0.1000 -0.0003
## 280 0.1004 nan 0.1000 -0.0002
## 300 0.0906 nan 0.1000 -0.0001
## 320 0.0813 nan 0.1000 -0.0002
## 340 0.0734 nan 0.1000 -0.0001
## 360 0.0662 nan 0.1000 -0.0002
## 380 0.0593 nan 0.1000 -0.0002
## 400 0.0534 nan 0.1000 -0.0001
## 420 0.0487 nan 0.1000 -0.0001
## 440 0.0448 nan 0.1000 -0.0001
## 460 0.0404 nan 0.1000 -0.0001
## 480 0.0374 nan 0.1000 -0.0001
## 500 0.0339 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0406
## 2 1.1568 nan 0.1000 0.0345
## 3 1.0983 nan 0.1000 0.0278
## 4 1.0415 nan 0.1000 0.0277
## 5 0.9966 nan 0.1000 0.0193
## 6 0.9524 nan 0.1000 0.0205
## 7 0.9113 nan 0.1000 0.0160
## 8 0.8782 nan 0.1000 0.0130
## 9 0.8468 nan 0.1000 0.0125
## 10 0.8200 nan 0.1000 0.0090
## 20 0.6569 nan 0.1000 0.0033
## 40 0.5171 nan 0.1000 -0.0008
## 60 0.4437 nan 0.1000 -0.0008
## 80 0.3709 nan 0.1000 0.0005
## 100 0.3186 nan 0.1000 -0.0011
## 120 0.2819 nan 0.1000 -0.0005
## 140 0.2460 nan 0.1000 -0.0011
## 160 0.2149 nan 0.1000 -0.0005
## 180 0.1917 nan 0.1000 -0.0009
## 200 0.1700 nan 0.1000 -0.0005
## 220 0.1504 nan 0.1000 -0.0006
## 240 0.1331 nan 0.1000 -0.0004
## 260 0.1196 nan 0.1000 -0.0005
## 280 0.1061 nan 0.1000 -0.0004
## 300 0.0950 nan 0.1000 -0.0003
## 320 0.0851 nan 0.1000 -0.0001
## 340 0.0775 nan 0.1000 -0.0002
## 360 0.0695 nan 0.1000 -0.0004
## 380 0.0625 nan 0.1000 -0.0002
## 400 0.0568 nan 0.1000 -0.0002
## 420 0.0512 nan 0.1000 -0.0002
## 440 0.0463 nan 0.1000 -0.0003
## 460 0.0418 nan 0.1000 -0.0001
## 480 0.0377 nan 0.1000 -0.0001
## 500 0.0345 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0414
## 2 1.1578 nan 0.1000 0.0373
## 3 1.0914 nan 0.1000 0.0292
## 4 1.0424 nan 0.1000 0.0222
## 5 0.9980 nan 0.1000 0.0191
## 6 0.9545 nan 0.1000 0.0185
## 7 0.9174 nan 0.1000 0.0156
## 8 0.8821 nan 0.1000 0.0147
## 9 0.8490 nan 0.1000 0.0131
## 10 0.8256 nan 0.1000 0.0095
## 20 0.6669 nan 0.1000 0.0015
## 40 0.5275 nan 0.1000 0.0001
## 60 0.4547 nan 0.1000 -0.0012
## 80 0.3877 nan 0.1000 -0.0002
## 100 0.3378 nan 0.1000 -0.0006
## 120 0.2921 nan 0.1000 -0.0002
## 140 0.2547 nan 0.1000 0.0001
## 160 0.2260 nan 0.1000 -0.0015
## 180 0.2027 nan 0.1000 -0.0001
## 200 0.1816 nan 0.1000 -0.0010
## 220 0.1607 nan 0.1000 -0.0010
## 240 0.1440 nan 0.1000 -0.0006
## 260 0.1286 nan 0.1000 -0.0001
## 280 0.1140 nan 0.1000 -0.0003
## 300 0.1025 nan 0.1000 -0.0005
## 320 0.0938 nan 0.1000 -0.0006
## 340 0.0839 nan 0.1000 -0.0004
## 360 0.0767 nan 0.1000 -0.0005
## 380 0.0696 nan 0.1000 -0.0005
## 400 0.0635 nan 0.1000 -0.0002
## 420 0.0571 nan 0.1000 -0.0003
## 440 0.0519 nan 0.1000 -0.0001
## 460 0.0469 nan 0.1000 -0.0001
## 480 0.0428 nan 0.1000 -0.0001
## 500 0.0389 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2223 nan 0.1000 0.0478
## 2 1.1443 nan 0.1000 0.0373
## 3 1.0760 nan 0.1000 0.0256
## 4 1.0172 nan 0.1000 0.0268
## 5 0.9744 nan 0.1000 0.0176
## 6 0.9348 nan 0.1000 0.0153
## 7 0.8922 nan 0.1000 0.0187
## 8 0.8599 nan 0.1000 0.0108
## 9 0.8292 nan 0.1000 0.0117
## 10 0.8009 nan 0.1000 0.0109
## 20 0.6324 nan 0.1000 0.0014
## 40 0.4825 nan 0.1000 -0.0011
## 60 0.3894 nan 0.1000 -0.0018
## 80 0.3234 nan 0.1000 0.0008
## 100 0.2707 nan 0.1000 -0.0004
## 120 0.2280 nan 0.1000 -0.0004
## 140 0.1949 nan 0.1000 -0.0005
## 160 0.1691 nan 0.1000 -0.0001
## 180 0.1436 nan 0.1000 -0.0003
## 200 0.1232 nan 0.1000 -0.0003
## 220 0.1076 nan 0.1000 -0.0003
## 240 0.0957 nan 0.1000 -0.0003
## 260 0.0825 nan 0.1000 -0.0002
## 280 0.0724 nan 0.1000 -0.0002
## 300 0.0632 nan 0.1000 -0.0002
## 320 0.0565 nan 0.1000 -0.0001
## 340 0.0492 nan 0.1000 -0.0001
## 360 0.0437 nan 0.1000 -0.0001
## 380 0.0387 nan 0.1000 -0.0001
## 400 0.0341 nan 0.1000 -0.0002
## 420 0.0302 nan 0.1000 -0.0000
## 440 0.0267 nan 0.1000 -0.0001
## 460 0.0236 nan 0.1000 -0.0001
## 480 0.0207 nan 0.1000 -0.0000
## 500 0.0184 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2207 nan 0.1000 0.0493
## 2 1.1468 nan 0.1000 0.0349
## 3 1.0759 nan 0.1000 0.0342
## 4 1.0255 nan 0.1000 0.0223
## 5 0.9775 nan 0.1000 0.0167
## 6 0.9384 nan 0.1000 0.0155
## 7 0.8989 nan 0.1000 0.0148
## 8 0.8645 nan 0.1000 0.0126
## 9 0.8369 nan 0.1000 0.0109
## 10 0.8064 nan 0.1000 0.0114
## 20 0.6389 nan 0.1000 0.0005
## 40 0.4858 nan 0.1000 -0.0003
## 60 0.4004 nan 0.1000 -0.0014
## 80 0.3318 nan 0.1000 -0.0022
## 100 0.2814 nan 0.1000 -0.0021
## 120 0.2390 nan 0.1000 -0.0003
## 140 0.2061 nan 0.1000 -0.0011
## 160 0.1737 nan 0.1000 -0.0005
## 180 0.1484 nan 0.1000 -0.0006
## 200 0.1276 nan 0.1000 -0.0005
## 220 0.1127 nan 0.1000 -0.0008
## 240 0.0996 nan 0.1000 -0.0002
## 260 0.0879 nan 0.1000 -0.0002
## 280 0.0771 nan 0.1000 -0.0003
## 300 0.0675 nan 0.1000 -0.0001
## 320 0.0597 nan 0.1000 -0.0001
## 340 0.0529 nan 0.1000 -0.0001
## 360 0.0474 nan 0.1000 -0.0002
## 380 0.0422 nan 0.1000 -0.0003
## 400 0.0370 nan 0.1000 -0.0002
## 420 0.0324 nan 0.1000 -0.0001
## 440 0.0287 nan 0.1000 -0.0001
## 460 0.0251 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0001
## 500 0.0195 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2288 nan 0.1000 0.0451
## 2 1.1429 nan 0.1000 0.0389
## 3 1.0721 nan 0.1000 0.0314
## 4 1.0175 nan 0.1000 0.0254
## 5 0.9648 nan 0.1000 0.0212
## 6 0.9212 nan 0.1000 0.0161
## 7 0.8886 nan 0.1000 0.0127
## 8 0.8552 nan 0.1000 0.0123
## 9 0.8265 nan 0.1000 0.0108
## 10 0.8015 nan 0.1000 0.0082
## 20 0.6378 nan 0.1000 0.0035
## 40 0.4956 nan 0.1000 -0.0005
## 60 0.4046 nan 0.1000 -0.0017
## 80 0.3421 nan 0.1000 -0.0009
## 100 0.2931 nan 0.1000 -0.0008
## 120 0.2520 nan 0.1000 -0.0020
## 140 0.2161 nan 0.1000 -0.0009
## 160 0.1872 nan 0.1000 -0.0009
## 180 0.1633 nan 0.1000 -0.0003
## 200 0.1425 nan 0.1000 -0.0009
## 220 0.1228 nan 0.1000 -0.0003
## 240 0.1090 nan 0.1000 -0.0006
## 260 0.0966 nan 0.1000 -0.0005
## 280 0.0853 nan 0.1000 -0.0003
## 300 0.0751 nan 0.1000 -0.0003
## 320 0.0667 nan 0.1000 -0.0003
## 340 0.0585 nan 0.1000 -0.0003
## 360 0.0523 nan 0.1000 -0.0001
## 380 0.0463 nan 0.1000 -0.0004
## 400 0.0415 nan 0.1000 -0.0002
## 420 0.0372 nan 0.1000 -0.0001
## 440 0.0334 nan 0.1000 -0.0001
## 460 0.0294 nan 0.1000 -0.0000
## 480 0.0265 nan 0.1000 -0.0002
## 500 0.0234 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0003
## 80 1.2564 nan 0.0010 0.0003
## 100 1.2424 nan 0.0010 0.0003
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2148 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1889 nan 0.0010 0.0002
## 200 1.1765 nan 0.0010 0.0003
## 220 1.1643 nan 0.0010 0.0003
## 240 1.1524 nan 0.0010 0.0003
## 260 1.1409 nan 0.0010 0.0002
## 280 1.1296 nan 0.0010 0.0003
## 300 1.1191 nan 0.0010 0.0003
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0985 nan 0.0010 0.0002
## 360 1.0885 nan 0.0010 0.0002
## 380 1.0791 nan 0.0010 0.0002
## 400 1.0698 nan 0.0010 0.0002
## 420 1.0606 nan 0.0010 0.0002
## 440 1.0517 nan 0.0010 0.0002
## 460 1.0430 nan 0.0010 0.0002
## 480 1.0346 nan 0.0010 0.0002
## 500 1.0263 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0003
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2153 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1891 nan 0.0010 0.0003
## 200 1.1766 nan 0.0010 0.0003
## 220 1.1644 nan 0.0010 0.0003
## 240 1.1527 nan 0.0010 0.0003
## 260 1.1413 nan 0.0010 0.0003
## 280 1.1300 nan 0.0010 0.0002
## 300 1.1193 nan 0.0010 0.0002
## 320 1.1093 nan 0.0010 0.0003
## 340 1.0992 nan 0.0010 0.0002
## 360 1.0893 nan 0.0010 0.0002
## 380 1.0796 nan 0.0010 0.0002
## 400 1.0703 nan 0.0010 0.0002
## 420 1.0612 nan 0.0010 0.0002
## 440 1.0522 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0349 nan 0.0010 0.0001
## 500 1.0270 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0003
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2884 nan 0.0010 0.0004
## 60 1.2730 nan 0.0010 0.0003
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2297 nan 0.0010 0.0003
## 140 1.2161 nan 0.0010 0.0003
## 160 1.2032 nan 0.0010 0.0003
## 180 1.1905 nan 0.0010 0.0003
## 200 1.1783 nan 0.0010 0.0002
## 220 1.1663 nan 0.0010 0.0003
## 240 1.1546 nan 0.0010 0.0003
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## 280 1.1323 nan 0.0010 0.0002
## 300 1.1216 nan 0.0010 0.0002
## 320 1.1111 nan 0.0010 0.0002
## 340 1.1011 nan 0.0010 0.0002
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## 380 1.0815 nan 0.0010 0.0002
## 400 1.0722 nan 0.0010 0.0002
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## 480 1.0374 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2688 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2371 nan 0.0010 0.0003
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2074 nan 0.0010 0.0003
## 160 1.1936 nan 0.0010 0.0003
## 180 1.1796 nan 0.0010 0.0003
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## 220 1.1533 nan 0.0010 0.0003
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## 380 1.0630 nan 0.0010 0.0002
## 400 1.0531 nan 0.0010 0.0002
## 420 1.0434 nan 0.0010 0.0002
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## 460 1.0249 nan 0.0010 0.0002
## 480 1.0156 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2689 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2377 nan 0.0010 0.0004
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2085 nan 0.0010 0.0004
## 160 1.1946 nan 0.0010 0.0002
## 180 1.1811 nan 0.0010 0.0003
## 200 1.1680 nan 0.0010 0.0002
## 220 1.1553 nan 0.0010 0.0003
## 240 1.1430 nan 0.0010 0.0002
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## 280 1.1194 nan 0.0010 0.0002
## 300 1.1080 nan 0.0010 0.0003
## 320 1.0969 nan 0.0010 0.0002
## 340 1.0863 nan 0.0010 0.0002
## 360 1.0757 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
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## 460 1.0276 nan 0.0010 0.0002
## 480 1.0185 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0003
## 20 1.3033 nan 0.0010 0.0003
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2546 nan 0.0010 0.0003
## 100 1.2398 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2111 nan 0.0010 0.0003
## 160 1.1972 nan 0.0010 0.0003
## 180 1.1839 nan 0.0010 0.0003
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## 220 1.1582 nan 0.0010 0.0003
## 240 1.1458 nan 0.0010 0.0002
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## 280 1.1220 nan 0.0010 0.0003
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## 320 1.0998 nan 0.0010 0.0002
## 340 1.0891 nan 0.0010 0.0002
## 360 1.0788 nan 0.0010 0.0002
## 380 1.0687 nan 0.0010 0.0002
## 400 1.0588 nan 0.0010 0.0002
## 420 1.0493 nan 0.0010 0.0002
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## 460 1.0308 nan 0.0010 0.0002
## 480 1.0221 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2497 nan 0.0010 0.0004
## 100 1.2337 nan 0.0010 0.0003
## 120 1.2178 nan 0.0010 0.0003
## 140 1.2025 nan 0.0010 0.0003
## 160 1.1874 nan 0.0010 0.0003
## 180 1.1729 nan 0.0010 0.0003
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## 240 1.1333 nan 0.0010 0.0003
## 260 1.1206 nan 0.0010 0.0002
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## 340 1.0735 nan 0.0010 0.0003
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## 380 1.0516 nan 0.0010 0.0002
## 400 1.0410 nan 0.0010 0.0003
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## 440 1.0210 nan 0.0010 0.0002
## 460 1.0115 nan 0.0010 0.0002
## 480 1.0021 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0003
## 60 1.2671 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2039 nan 0.0010 0.0003
## 160 1.1893 nan 0.0010 0.0003
## 180 1.1750 nan 0.0010 0.0003
## 200 1.1616 nan 0.0010 0.0003
## 220 1.1481 nan 0.0010 0.0002
## 240 1.1351 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0003
## 280 1.1102 nan 0.0010 0.0003
## 300 1.0983 nan 0.0010 0.0002
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0542 nan 0.0010 0.0003
## 400 1.0437 nan 0.0010 0.0002
## 420 1.0337 nan 0.0010 0.0002
## 440 1.0238 nan 0.0010 0.0002
## 460 1.0143 nan 0.0010 0.0002
## 480 1.0051 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0003
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0004
## 120 1.2203 nan 0.0010 0.0004
## 140 1.2054 nan 0.0010 0.0003
## 160 1.1909 nan 0.0010 0.0003
## 180 1.1770 nan 0.0010 0.0003
## 200 1.1637 nan 0.0010 0.0003
## 220 1.1507 nan 0.0010 0.0003
## 240 1.1380 nan 0.0010 0.0002
## 260 1.1255 nan 0.0010 0.0002
## 280 1.1135 nan 0.0010 0.0003
## 300 1.1019 nan 0.0010 0.0003
## 320 1.0906 nan 0.0010 0.0002
## 340 1.0797 nan 0.0010 0.0003
## 360 1.0688 nan 0.0010 0.0002
## 380 1.0583 nan 0.0010 0.0002
## 400 1.0483 nan 0.0010 0.0002
## 420 1.0384 nan 0.0010 0.0002
## 440 1.0284 nan 0.0010 0.0002
## 460 1.0191 nan 0.0010 0.0002
## 480 1.0102 nan 0.0010 0.0002
## 500 1.0013 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0038
## 2 1.3043 nan 0.0100 0.0034
## 3 1.2963 nan 0.0100 0.0037
## 4 1.2875 nan 0.0100 0.0041
## 5 1.2807 nan 0.0100 0.0031
## 6 1.2736 nan 0.0100 0.0028
## 7 1.2661 nan 0.0100 0.0034
## 8 1.2580 nan 0.0100 0.0034
## 9 1.2505 nan 0.0100 0.0031
## 10 1.2426 nan 0.0100 0.0037
## 20 1.1759 nan 0.0100 0.0027
## 40 1.0692 nan 0.0100 0.0017
## 60 0.9874 nan 0.0100 0.0016
## 80 0.9246 nan 0.0100 0.0010
## 100 0.8693 nan 0.0100 0.0011
## 120 0.8263 nan 0.0100 0.0007
## 140 0.7894 nan 0.0100 0.0007
## 160 0.7580 nan 0.0100 0.0003
## 180 0.7326 nan 0.0100 0.0004
## 200 0.7114 nan 0.0100 0.0001
## 220 0.6919 nan 0.0100 0.0002
## 240 0.6736 nan 0.0100 0.0001
## 260 0.6579 nan 0.0100 0.0001
## 280 0.6432 nan 0.0100 0.0001
## 300 0.6317 nan 0.0100 -0.0000
## 320 0.6200 nan 0.0100 0.0001
## 340 0.6083 nan 0.0100 -0.0001
## 360 0.5966 nan 0.0100 -0.0000
## 380 0.5871 nan 0.0100 -0.0000
## 400 0.5775 nan 0.0100 -0.0000
## 420 0.5674 nan 0.0100 -0.0001
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## 460 0.5503 nan 0.0100 -0.0000
## 480 0.5414 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3037 nan 0.0100 0.0033
## 3 1.2953 nan 0.0100 0.0040
## 4 1.2874 nan 0.0100 0.0036
## 5 1.2794 nan 0.0100 0.0039
## 6 1.2716 nan 0.0100 0.0037
## 7 1.2640 nan 0.0100 0.0036
## 8 1.2567 nan 0.0100 0.0033
## 9 1.2491 nan 0.0100 0.0039
## 10 1.2423 nan 0.0100 0.0032
## 20 1.1740 nan 0.0100 0.0023
## 40 1.0686 nan 0.0100 0.0022
## 60 0.9868 nan 0.0100 0.0014
## 80 0.9221 nan 0.0100 0.0010
## 100 0.8702 nan 0.0100 0.0009
## 120 0.8272 nan 0.0100 0.0005
## 140 0.7930 nan 0.0100 0.0004
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## 180 0.7391 nan 0.0100 0.0003
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## 220 0.6954 nan 0.0100 0.0002
## 240 0.6778 nan 0.0100 0.0003
## 260 0.6619 nan 0.0100 0.0004
## 280 0.6488 nan 0.0100 -0.0000
## 300 0.6360 nan 0.0100 -0.0002
## 320 0.6245 nan 0.0100 0.0001
## 340 0.6131 nan 0.0100 0.0000
## 360 0.6026 nan 0.0100 -0.0001
## 380 0.5918 nan 0.0100 -0.0000
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## 460 0.5524 nan 0.0100 0.0001
## 480 0.5439 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3132 nan 0.0100 0.0038
## 2 1.3053 nan 0.0100 0.0039
## 3 1.2965 nan 0.0100 0.0039
## 4 1.2890 nan 0.0100 0.0031
## 5 1.2813 nan 0.0100 0.0030
## 6 1.2733 nan 0.0100 0.0036
## 7 1.2654 nan 0.0100 0.0037
## 8 1.2582 nan 0.0100 0.0034
## 9 1.2506 nan 0.0100 0.0033
## 10 1.2428 nan 0.0100 0.0034
## 20 1.1791 nan 0.0100 0.0023
## 40 1.0725 nan 0.0100 0.0016
## 60 0.9909 nan 0.0100 0.0017
## 80 0.9253 nan 0.0100 0.0010
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## 140 0.7957 nan 0.0100 0.0006
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## 180 0.7418 nan 0.0100 0.0001
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## 220 0.7011 nan 0.0100 0.0001
## 240 0.6841 nan 0.0100 0.0002
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## 280 0.6549 nan 0.0100 0.0001
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## 320 0.6295 nan 0.0100 0.0001
## 340 0.6187 nan 0.0100 0.0000
## 360 0.6070 nan 0.0100 0.0000
## 380 0.5969 nan 0.0100 -0.0000
## 400 0.5875 nan 0.0100 -0.0000
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## 440 0.5686 nan 0.0100 0.0000
## 460 0.5592 nan 0.0100 -0.0001
## 480 0.5509 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2929 nan 0.0100 0.0042
## 4 1.2841 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0039
## 6 1.2680 nan 0.0100 0.0037
## 7 1.2605 nan 0.0100 0.0031
## 8 1.2519 nan 0.0100 0.0036
## 9 1.2443 nan 0.0100 0.0033
## 10 1.2370 nan 0.0100 0.0032
## 20 1.1662 nan 0.0100 0.0031
## 40 1.0536 nan 0.0100 0.0017
## 60 0.9664 nan 0.0100 0.0018
## 80 0.8991 nan 0.0100 0.0013
## 100 0.8440 nan 0.0100 0.0010
## 120 0.7983 nan 0.0100 0.0008
## 140 0.7608 nan 0.0100 0.0005
## 160 0.7283 nan 0.0100 0.0002
## 180 0.7020 nan 0.0100 0.0002
## 200 0.6778 nan 0.0100 0.0001
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## 280 0.6020 nan 0.0100 0.0000
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## 340 0.5613 nan 0.0100 0.0001
## 360 0.5488 nan 0.0100 0.0002
## 380 0.5371 nan 0.0100 -0.0001
## 400 0.5263 nan 0.0100 -0.0000
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## 440 0.5058 nan 0.0100 -0.0000
## 460 0.4952 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0042
## 2 1.3034 nan 0.0100 0.0044
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2858 nan 0.0100 0.0039
## 5 1.2773 nan 0.0100 0.0039
## 6 1.2687 nan 0.0100 0.0035
## 7 1.2601 nan 0.0100 0.0040
## 8 1.2519 nan 0.0100 0.0038
## 9 1.2444 nan 0.0100 0.0034
## 10 1.2362 nan 0.0100 0.0036
## 20 1.1650 nan 0.0100 0.0031
## 40 1.0547 nan 0.0100 0.0018
## 60 0.9698 nan 0.0100 0.0016
## 80 0.9000 nan 0.0100 0.0015
## 100 0.8445 nan 0.0100 0.0010
## 120 0.7998 nan 0.0100 0.0008
## 140 0.7636 nan 0.0100 0.0005
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## 180 0.7072 nan 0.0100 0.0003
## 200 0.6821 nan 0.0100 0.0002
## 220 0.6614 nan 0.0100 0.0004
## 240 0.6425 nan 0.0100 0.0002
## 260 0.6257 nan 0.0100 0.0002
## 280 0.6109 nan 0.0100 -0.0000
## 300 0.5961 nan 0.0100 0.0001
## 320 0.5829 nan 0.0100 -0.0000
## 340 0.5701 nan 0.0100 -0.0003
## 360 0.5584 nan 0.0100 -0.0001
## 380 0.5468 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0038
## 2 1.3030 nan 0.0100 0.0042
## 3 1.2951 nan 0.0100 0.0036
## 4 1.2869 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0040
## 6 1.2703 nan 0.0100 0.0034
## 7 1.2624 nan 0.0100 0.0034
## 8 1.2541 nan 0.0100 0.0035
## 9 1.2467 nan 0.0100 0.0029
## 10 1.2394 nan 0.0100 0.0031
## 20 1.1696 nan 0.0100 0.0027
## 40 1.0609 nan 0.0100 0.0017
## 60 0.9767 nan 0.0100 0.0013
## 80 0.9090 nan 0.0100 0.0012
## 100 0.8560 nan 0.0100 0.0009
## 120 0.8103 nan 0.0100 0.0007
## 140 0.7744 nan 0.0100 0.0007
## 160 0.7435 nan 0.0100 0.0003
## 180 0.7164 nan 0.0100 0.0004
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## 320 0.5906 nan 0.0100 -0.0001
## 340 0.5778 nan 0.0100 -0.0000
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## 380 0.5542 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0045
## 2 1.3009 nan 0.0100 0.0042
## 3 1.2905 nan 0.0100 0.0043
## 4 1.2822 nan 0.0100 0.0037
## 5 1.2722 nan 0.0100 0.0039
## 6 1.2637 nan 0.0100 0.0042
## 7 1.2560 nan 0.0100 0.0033
## 8 1.2481 nan 0.0100 0.0036
## 9 1.2403 nan 0.0100 0.0034
## 10 1.2320 nan 0.0100 0.0034
## 20 1.1556 nan 0.0100 0.0031
## 40 1.0387 nan 0.0100 0.0024
## 60 0.9495 nan 0.0100 0.0015
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## 240 0.6033 nan 0.0100 -0.0000
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## 460 0.4527 nan 0.0100 -0.0001
## 480 0.4426 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0043
## 2 1.3012 nan 0.0100 0.0044
## 3 1.2927 nan 0.0100 0.0038
## 4 1.2842 nan 0.0100 0.0043
## 5 1.2754 nan 0.0100 0.0042
## 6 1.2666 nan 0.0100 0.0039
## 7 1.2583 nan 0.0100 0.0034
## 8 1.2500 nan 0.0100 0.0039
## 9 1.2413 nan 0.0100 0.0038
## 10 1.2333 nan 0.0100 0.0036
## 20 1.1599 nan 0.0100 0.0027
## 40 1.0445 nan 0.0100 0.0023
## 60 0.9554 nan 0.0100 0.0016
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## 240 0.6136 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3027 nan 0.0100 0.0036
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2847 nan 0.0100 0.0037
## 5 1.2760 nan 0.0100 0.0042
## 6 1.2673 nan 0.0100 0.0035
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## 60 0.9606 nan 0.0100 0.0018
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## 120 0.7898 nan 0.0100 0.0005
## 140 0.7508 nan 0.0100 0.0005
## 160 0.7183 nan 0.0100 0.0006
## 180 0.6911 nan 0.0100 0.0002
## 200 0.6658 nan 0.0100 0.0002
## 220 0.6433 nan 0.0100 0.0002
## 240 0.6222 nan 0.0100 0.0000
## 260 0.6032 nan 0.0100 -0.0001
## 280 0.5865 nan 0.0100 -0.0000
## 300 0.5701 nan 0.0100 0.0001
## 320 0.5549 nan 0.0100 0.0000
## 340 0.5417 nan 0.0100 -0.0002
## 360 0.5286 nan 0.0100 0.0001
## 380 0.5171 nan 0.0100 0.0000
## 400 0.5054 nan 0.0100 -0.0000
## 420 0.4941 nan 0.0100 -0.0002
## 440 0.4838 nan 0.0100 -0.0001
## 460 0.4732 nan 0.0100 0.0001
## 480 0.4629 nan 0.0100 -0.0001
## 500 0.4526 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2447 nan 0.1000 0.0344
## 2 1.1709 nan 0.1000 0.0304
## 3 1.1140 nan 0.1000 0.0274
## 4 1.0649 nan 0.1000 0.0200
## 5 1.0277 nan 0.1000 0.0168
## 6 0.9906 nan 0.1000 0.0150
## 7 0.9513 nan 0.1000 0.0145
## 8 0.9248 nan 0.1000 0.0106
## 9 0.8956 nan 0.1000 0.0122
## 10 0.8747 nan 0.1000 0.0066
## 20 0.7203 nan 0.1000 0.0031
## 40 0.5863 nan 0.1000 -0.0004
## 60 0.5004 nan 0.1000 0.0009
## 80 0.4303 nan 0.1000 -0.0007
## 100 0.3850 nan 0.1000 -0.0003
## 120 0.3405 nan 0.1000 -0.0003
## 140 0.3035 nan 0.1000 -0.0006
## 160 0.2707 nan 0.1000 -0.0003
## 180 0.2432 nan 0.1000 -0.0005
## 200 0.2195 nan 0.1000 0.0001
## 220 0.1980 nan 0.1000 -0.0008
## 240 0.1773 nan 0.1000 -0.0007
## 260 0.1621 nan 0.1000 -0.0005
## 280 0.1473 nan 0.1000 -0.0005
## 300 0.1329 nan 0.1000 -0.0000
## 320 0.1208 nan 0.1000 0.0003
## 340 0.1102 nan 0.1000 -0.0001
## 360 0.1007 nan 0.1000 -0.0004
## 380 0.0927 nan 0.1000 -0.0001
## 400 0.0843 nan 0.1000 -0.0002
## 420 0.0777 nan 0.1000 -0.0003
## 440 0.0715 nan 0.1000 -0.0002
## 460 0.0647 nan 0.1000 -0.0002
## 480 0.0591 nan 0.1000 -0.0001
## 500 0.0544 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2460 nan 0.1000 0.0332
## 2 1.1701 nan 0.1000 0.0333
## 3 1.1088 nan 0.1000 0.0292
## 4 1.0586 nan 0.1000 0.0215
## 5 1.0169 nan 0.1000 0.0173
## 6 0.9822 nan 0.1000 0.0156
## 7 0.9517 nan 0.1000 0.0129
## 8 0.9211 nan 0.1000 0.0117
## 9 0.8918 nan 0.1000 0.0135
## 10 0.8710 nan 0.1000 0.0070
## 20 0.7252 nan 0.1000 0.0004
## 40 0.5905 nan 0.1000 -0.0011
## 60 0.5149 nan 0.1000 -0.0009
## 80 0.4447 nan 0.1000 0.0000
## 100 0.3942 nan 0.1000 -0.0018
## 120 0.3556 nan 0.1000 -0.0011
## 140 0.3178 nan 0.1000 0.0005
## 160 0.2871 nan 0.1000 -0.0004
## 180 0.2540 nan 0.1000 -0.0007
## 200 0.2316 nan 0.1000 -0.0008
## 220 0.2095 nan 0.1000 -0.0005
## 240 0.1890 nan 0.1000 -0.0001
## 260 0.1707 nan 0.1000 -0.0003
## 280 0.1542 nan 0.1000 -0.0001
## 300 0.1404 nan 0.1000 -0.0003
## 320 0.1283 nan 0.1000 -0.0005
## 340 0.1170 nan 0.1000 -0.0003
## 360 0.1075 nan 0.1000 -0.0002
## 380 0.0988 nan 0.1000 -0.0003
## 400 0.0905 nan 0.1000 -0.0003
## 420 0.0836 nan 0.1000 -0.0002
## 440 0.0766 nan 0.1000 0.0001
## 460 0.0707 nan 0.1000 0.0000
## 480 0.0658 nan 0.1000 -0.0003
## 500 0.0605 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2390 nan 0.1000 0.0361
## 2 1.1672 nan 0.1000 0.0327
## 3 1.1118 nan 0.1000 0.0198
## 4 1.0591 nan 0.1000 0.0213
## 5 1.0184 nan 0.1000 0.0160
## 6 0.9817 nan 0.1000 0.0178
## 7 0.9459 nan 0.1000 0.0131
## 8 0.9151 nan 0.1000 0.0142
## 9 0.8890 nan 0.1000 0.0112
## 10 0.8648 nan 0.1000 0.0106
## 20 0.7173 nan 0.1000 0.0007
## 40 0.5817 nan 0.1000 -0.0001
## 60 0.4991 nan 0.1000 -0.0006
## 80 0.4376 nan 0.1000 -0.0006
## 100 0.3834 nan 0.1000 0.0002
## 120 0.3418 nan 0.1000 0.0002
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2820 nan 0.1000 -0.0009
## 180 0.2514 nan 0.1000 -0.0003
## 200 0.2267 nan 0.1000 -0.0002
## 220 0.2052 nan 0.1000 -0.0006
## 240 0.1848 nan 0.1000 -0.0000
## 260 0.1680 nan 0.1000 -0.0003
## 280 0.1523 nan 0.1000 -0.0008
## 300 0.1379 nan 0.1000 -0.0004
## 320 0.1253 nan 0.1000 -0.0004
## 340 0.1150 nan 0.1000 -0.0003
## 360 0.1045 nan 0.1000 -0.0003
## 380 0.0962 nan 0.1000 -0.0003
## 400 0.0890 nan 0.1000 -0.0001
## 420 0.0821 nan 0.1000 -0.0001
## 440 0.0756 nan 0.1000 -0.0003
## 460 0.0694 nan 0.1000 -0.0001
## 480 0.0636 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0408
## 2 1.1597 nan 0.1000 0.0315
## 3 1.0987 nan 0.1000 0.0278
## 4 1.0450 nan 0.1000 0.0214
## 5 1.0017 nan 0.1000 0.0177
## 6 0.9608 nan 0.1000 0.0154
## 7 0.9265 nan 0.1000 0.0121
## 8 0.8977 nan 0.1000 0.0086
## 9 0.8666 nan 0.1000 0.0122
## 10 0.8408 nan 0.1000 0.0113
## 20 0.6877 nan 0.1000 0.0009
## 40 0.5405 nan 0.1000 0.0007
## 60 0.4448 nan 0.1000 -0.0004
## 80 0.3753 nan 0.1000 -0.0005
## 100 0.3214 nan 0.1000 -0.0012
## 120 0.2741 nan 0.1000 0.0003
## 140 0.2380 nan 0.1000 0.0001
## 160 0.2046 nan 0.1000 -0.0004
## 180 0.1775 nan 0.1000 -0.0003
## 200 0.1553 nan 0.1000 -0.0000
## 220 0.1353 nan 0.1000 -0.0002
## 240 0.1210 nan 0.1000 -0.0002
## 260 0.1057 nan 0.1000 -0.0005
## 280 0.0947 nan 0.1000 -0.0002
## 300 0.0843 nan 0.1000 -0.0001
## 320 0.0756 nan 0.1000 -0.0002
## 340 0.0681 nan 0.1000 -0.0003
## 360 0.0609 nan 0.1000 -0.0000
## 380 0.0547 nan 0.1000 -0.0001
## 400 0.0495 nan 0.1000 -0.0001
## 420 0.0447 nan 0.1000 -0.0000
## 440 0.0409 nan 0.1000 -0.0001
## 460 0.0367 nan 0.1000 -0.0000
## 480 0.0331 nan 0.1000 -0.0001
## 500 0.0303 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0394
## 2 1.1647 nan 0.1000 0.0297
## 3 1.1021 nan 0.1000 0.0290
## 4 1.0490 nan 0.1000 0.0249
## 5 1.0037 nan 0.1000 0.0196
## 6 0.9635 nan 0.1000 0.0151
## 7 0.9272 nan 0.1000 0.0135
## 8 0.8959 nan 0.1000 0.0123
## 9 0.8694 nan 0.1000 0.0111
## 10 0.8446 nan 0.1000 0.0089
## 20 0.6826 nan 0.1000 0.0016
## 40 0.5282 nan 0.1000 -0.0014
## 60 0.4499 nan 0.1000 -0.0013
## 80 0.3851 nan 0.1000 -0.0002
## 100 0.3223 nan 0.1000 -0.0000
## 120 0.2778 nan 0.1000 0.0001
## 140 0.2415 nan 0.1000 -0.0006
## 160 0.2113 nan 0.1000 -0.0006
## 180 0.1882 nan 0.1000 -0.0008
## 200 0.1636 nan 0.1000 -0.0004
## 220 0.1440 nan 0.1000 -0.0002
## 240 0.1279 nan 0.1000 -0.0008
## 260 0.1138 nan 0.1000 -0.0003
## 280 0.1007 nan 0.1000 -0.0001
## 300 0.0889 nan 0.1000 -0.0002
## 320 0.0799 nan 0.1000 -0.0001
## 340 0.0711 nan 0.1000 -0.0004
## 360 0.0637 nan 0.1000 -0.0002
## 380 0.0575 nan 0.1000 -0.0002
## 400 0.0513 nan 0.1000 -0.0001
## 420 0.0456 nan 0.1000 -0.0001
## 440 0.0409 nan 0.1000 -0.0001
## 460 0.0361 nan 0.1000 -0.0002
## 480 0.0325 nan 0.1000 -0.0001
## 500 0.0294 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.1000 0.0346
## 2 1.1721 nan 0.1000 0.0253
## 3 1.1088 nan 0.1000 0.0282
## 4 1.0600 nan 0.1000 0.0185
## 5 1.0193 nan 0.1000 0.0160
## 6 0.9743 nan 0.1000 0.0195
## 7 0.9419 nan 0.1000 0.0129
## 8 0.9090 nan 0.1000 0.0108
## 9 0.8768 nan 0.1000 0.0127
## 10 0.8515 nan 0.1000 0.0094
## 20 0.6863 nan 0.1000 0.0011
## 40 0.5479 nan 0.1000 0.0000
## 60 0.4605 nan 0.1000 -0.0006
## 80 0.3956 nan 0.1000 -0.0017
## 100 0.3372 nan 0.1000 -0.0006
## 120 0.2905 nan 0.1000 -0.0009
## 140 0.2571 nan 0.1000 -0.0007
## 160 0.2269 nan 0.1000 -0.0006
## 180 0.2005 nan 0.1000 -0.0004
## 200 0.1803 nan 0.1000 -0.0004
## 220 0.1613 nan 0.1000 -0.0001
## 240 0.1436 nan 0.1000 -0.0006
## 260 0.1273 nan 0.1000 -0.0004
## 280 0.1127 nan 0.1000 -0.0004
## 300 0.1007 nan 0.1000 -0.0002
## 320 0.0901 nan 0.1000 -0.0004
## 340 0.0812 nan 0.1000 -0.0004
## 360 0.0734 nan 0.1000 -0.0003
## 380 0.0657 nan 0.1000 -0.0003
## 400 0.0591 nan 0.1000 -0.0002
## 420 0.0524 nan 0.1000 -0.0001
## 440 0.0477 nan 0.1000 -0.0001
## 460 0.0430 nan 0.1000 -0.0001
## 480 0.0386 nan 0.1000 -0.0001
## 500 0.0347 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2446 nan 0.1000 0.0313
## 2 1.1666 nan 0.1000 0.0366
## 3 1.1058 nan 0.1000 0.0253
## 4 1.0503 nan 0.1000 0.0184
## 5 0.9984 nan 0.1000 0.0225
## 6 0.9610 nan 0.1000 0.0163
## 7 0.9248 nan 0.1000 0.0142
## 8 0.8937 nan 0.1000 0.0121
## 9 0.8664 nan 0.1000 0.0082
## 10 0.8373 nan 0.1000 0.0134
## 20 0.6630 nan 0.1000 0.0017
## 40 0.4969 nan 0.1000 0.0015
## 60 0.3977 nan 0.1000 0.0007
## 80 0.3258 nan 0.1000 -0.0012
## 100 0.2724 nan 0.1000 0.0001
## 120 0.2302 nan 0.1000 -0.0005
## 140 0.1967 nan 0.1000 -0.0007
## 160 0.1673 nan 0.1000 -0.0004
## 180 0.1435 nan 0.1000 -0.0003
## 200 0.1211 nan 0.1000 -0.0003
## 220 0.1044 nan 0.1000 -0.0001
## 240 0.0911 nan 0.1000 -0.0001
## 260 0.0785 nan 0.1000 -0.0000
## 280 0.0701 nan 0.1000 -0.0003
## 300 0.0604 nan 0.1000 -0.0001
## 320 0.0520 nan 0.1000 -0.0000
## 340 0.0450 nan 0.1000 -0.0001
## 360 0.0385 nan 0.1000 -0.0001
## 380 0.0340 nan 0.1000 -0.0001
## 400 0.0303 nan 0.1000 -0.0001
## 420 0.0268 nan 0.1000 -0.0000
## 440 0.0235 nan 0.1000 0.0000
## 460 0.0210 nan 0.1000 -0.0000
## 480 0.0187 nan 0.1000 -0.0001
## 500 0.0166 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0376
## 2 1.1580 nan 0.1000 0.0343
## 3 1.0980 nan 0.1000 0.0255
## 4 1.0421 nan 0.1000 0.0262
## 5 0.9911 nan 0.1000 0.0217
## 6 0.9471 nan 0.1000 0.0189
## 7 0.9079 nan 0.1000 0.0174
## 8 0.8763 nan 0.1000 0.0116
## 9 0.8459 nan 0.1000 0.0112
## 10 0.8177 nan 0.1000 0.0092
## 20 0.6492 nan 0.1000 0.0028
## 40 0.4992 nan 0.1000 -0.0002
## 60 0.4036 nan 0.1000 -0.0012
## 80 0.3344 nan 0.1000 -0.0012
## 100 0.2751 nan 0.1000 -0.0009
## 120 0.2263 nan 0.1000 -0.0008
## 140 0.1943 nan 0.1000 -0.0002
## 160 0.1646 nan 0.1000 -0.0008
## 180 0.1420 nan 0.1000 -0.0005
## 200 0.1216 nan 0.1000 0.0001
## 220 0.1046 nan 0.1000 -0.0002
## 240 0.0907 nan 0.1000 -0.0004
## 260 0.0790 nan 0.1000 -0.0003
## 280 0.0693 nan 0.1000 -0.0002
## 300 0.0601 nan 0.1000 -0.0002
## 320 0.0526 nan 0.1000 -0.0002
## 340 0.0463 nan 0.1000 -0.0002
## 360 0.0408 nan 0.1000 -0.0003
## 380 0.0357 nan 0.1000 -0.0001
## 400 0.0316 nan 0.1000 -0.0001
## 420 0.0273 nan 0.1000 -0.0000
## 440 0.0239 nan 0.1000 -0.0001
## 460 0.0207 nan 0.1000 -0.0001
## 480 0.0185 nan 0.1000 -0.0000
## 500 0.0160 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2304 nan 0.1000 0.0442
## 2 1.1536 nan 0.1000 0.0362
## 3 1.0943 nan 0.1000 0.0225
## 4 1.0438 nan 0.1000 0.0247
## 5 0.9978 nan 0.1000 0.0213
## 6 0.9547 nan 0.1000 0.0164
## 7 0.9221 nan 0.1000 0.0144
## 8 0.8887 nan 0.1000 0.0135
## 9 0.8562 nan 0.1000 0.0139
## 10 0.8274 nan 0.1000 0.0112
## 20 0.6546 nan 0.1000 0.0021
## 40 0.5011 nan 0.1000 0.0011
## 60 0.4108 nan 0.1000 -0.0003
## 80 0.3401 nan 0.1000 -0.0002
## 100 0.2841 nan 0.1000 -0.0005
## 120 0.2397 nan 0.1000 -0.0007
## 140 0.2001 nan 0.1000 -0.0003
## 160 0.1720 nan 0.1000 -0.0005
## 180 0.1511 nan 0.1000 -0.0008
## 200 0.1296 nan 0.1000 -0.0006
## 220 0.1114 nan 0.1000 -0.0004
## 240 0.0960 nan 0.1000 -0.0000
## 260 0.0826 nan 0.1000 -0.0002
## 280 0.0710 nan 0.1000 -0.0003
## 300 0.0622 nan 0.1000 -0.0004
## 320 0.0546 nan 0.1000 -0.0001
## 340 0.0480 nan 0.1000 -0.0001
## 360 0.0422 nan 0.1000 -0.0002
## 380 0.0377 nan 0.1000 -0.0001
## 400 0.0330 nan 0.1000 -0.0001
## 420 0.0293 nan 0.1000 -0.0001
## 440 0.0256 nan 0.1000 -0.0001
## 460 0.0227 nan 0.1000 -0.0001
## 480 0.0202 nan 0.1000 -0.0000
## 500 0.0177 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0003
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2564 nan 0.0010 0.0003
## 100 1.2414 nan 0.0010 0.0003
## 120 1.2271 nan 0.0010 0.0003
## 140 1.2134 nan 0.0010 0.0003
## 160 1.2000 nan 0.0010 0.0003
## 180 1.1870 nan 0.0010 0.0003
## 200 1.1741 nan 0.0010 0.0002
## 220 1.1619 nan 0.0010 0.0002
## 240 1.1500 nan 0.0010 0.0002
## 260 1.1385 nan 0.0010 0.0003
## 280 1.1271 nan 0.0010 0.0003
## 300 1.1164 nan 0.0010 0.0002
## 320 1.1058 nan 0.0010 0.0002
## 340 1.0956 nan 0.0010 0.0002
## 360 1.0857 nan 0.0010 0.0002
## 380 1.0759 nan 0.0010 0.0002
## 400 1.0664 nan 0.0010 0.0002
## 420 1.0572 nan 0.0010 0.0002
## 440 1.0482 nan 0.0010 0.0002
## 460 1.0392 nan 0.0010 0.0002
## 480 1.0307 nan 0.0010 0.0002
## 500 1.0225 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0004
## 80 1.2563 nan 0.0010 0.0003
## 100 1.2413 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2133 nan 0.0010 0.0003
## 160 1.1999 nan 0.0010 0.0003
## 180 1.1868 nan 0.0010 0.0003
## 200 1.1742 nan 0.0010 0.0002
## 220 1.1617 nan 0.0010 0.0003
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0002
## 280 1.1270 nan 0.0010 0.0003
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1056 nan 0.0010 0.0002
## 340 1.0952 nan 0.0010 0.0002
## 360 1.0852 nan 0.0010 0.0002
## 380 1.0755 nan 0.0010 0.0002
## 400 1.0660 nan 0.0010 0.0002
## 420 1.0568 nan 0.0010 0.0002
## 440 1.0477 nan 0.0010 0.0002
## 460 1.0390 nan 0.0010 0.0002
## 480 1.0308 nan 0.0010 0.0001
## 500 1.0225 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0003
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2562 nan 0.0010 0.0004
## 100 1.2410 nan 0.0010 0.0004
## 120 1.2271 nan 0.0010 0.0003
## 140 1.2132 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0002
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1738 nan 0.0010 0.0003
## 220 1.1615 nan 0.0010 0.0002
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0003
## 280 1.1271 nan 0.0010 0.0003
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1054 nan 0.0010 0.0002
## 340 1.0952 nan 0.0010 0.0002
## 360 1.0852 nan 0.0010 0.0002
## 380 1.0756 nan 0.0010 0.0002
## 400 1.0663 nan 0.0010 0.0002
## 420 1.0573 nan 0.0010 0.0002
## 440 1.0483 nan 0.0010 0.0002
## 460 1.0395 nan 0.0010 0.0002
## 480 1.0309 nan 0.0010 0.0002
## 500 1.0227 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0003
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0005
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2673 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2349 nan 0.0010 0.0004
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0003
## 180 1.1766 nan 0.0010 0.0003
## 200 1.1629 nan 0.0010 0.0003
## 220 1.1496 nan 0.0010 0.0003
## 240 1.1369 nan 0.0010 0.0003
## 260 1.1247 nan 0.0010 0.0003
## 280 1.1125 nan 0.0010 0.0003
## 300 1.1008 nan 0.0010 0.0003
## 320 1.0893 nan 0.0010 0.0002
## 340 1.0786 nan 0.0010 0.0002
## 360 1.0681 nan 0.0010 0.0002
## 380 1.0576 nan 0.0010 0.0002
## 400 1.0473 nan 0.0010 0.0002
## 420 1.0375 nan 0.0010 0.0002
## 440 1.0281 nan 0.0010 0.0002
## 460 1.0188 nan 0.0010 0.0002
## 480 1.0096 nan 0.0010 0.0002
## 500 1.0010 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2511 nan 0.0010 0.0003
## 100 1.2351 nan 0.0010 0.0003
## 120 1.2196 nan 0.0010 0.0004
## 140 1.2049 nan 0.0010 0.0003
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1770 nan 0.0010 0.0003
## 200 1.1635 nan 0.0010 0.0003
## 220 1.1504 nan 0.0010 0.0003
## 240 1.1380 nan 0.0010 0.0003
## 260 1.1258 nan 0.0010 0.0003
## 280 1.1139 nan 0.0010 0.0003
## 300 1.1023 nan 0.0010 0.0002
## 320 1.0912 nan 0.0010 0.0002
## 340 1.0800 nan 0.0010 0.0003
## 360 1.0693 nan 0.0010 0.0002
## 380 1.0588 nan 0.0010 0.0002
## 400 1.0487 nan 0.0010 0.0002
## 420 1.0388 nan 0.0010 0.0002
## 440 1.0294 nan 0.0010 0.0002
## 460 1.0204 nan 0.0010 0.0002
## 480 1.0113 nan 0.0010 0.0002
## 500 1.0026 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2362 nan 0.0010 0.0003
## 120 1.2212 nan 0.0010 0.0003
## 140 1.2062 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1777 nan 0.0010 0.0003
## 200 1.1644 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1389 nan 0.0010 0.0003
## 260 1.1266 nan 0.0010 0.0003
## 280 1.1149 nan 0.0010 0.0002
## 300 1.1035 nan 0.0010 0.0002
## 320 1.0924 nan 0.0010 0.0002
## 340 1.0817 nan 0.0010 0.0002
## 360 1.0712 nan 0.0010 0.0002
## 380 1.0608 nan 0.0010 0.0002
## 400 1.0506 nan 0.0010 0.0002
## 420 1.0411 nan 0.0010 0.0002
## 440 1.0316 nan 0.0010 0.0002
## 460 1.0223 nan 0.0010 0.0002
## 480 1.0132 nan 0.0010 0.0002
## 500 1.0045 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3176 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3156 nan 0.0010 0.0005
## 6 1.3146 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3117 nan 0.0010 0.0005
## 10 1.3107 nan 0.0010 0.0004
## 20 1.3012 nan 0.0010 0.0004
## 40 1.2827 nan 0.0010 0.0004
## 60 1.2648 nan 0.0010 0.0004
## 80 1.2476 nan 0.0010 0.0004
## 100 1.2312 nan 0.0010 0.0003
## 120 1.2154 nan 0.0010 0.0003
## 140 1.1997 nan 0.0010 0.0004
## 160 1.1848 nan 0.0010 0.0004
## 180 1.1705 nan 0.0010 0.0003
## 200 1.1563 nan 0.0010 0.0003
## 220 1.1424 nan 0.0010 0.0003
## 240 1.1291 nan 0.0010 0.0003
## 260 1.1162 nan 0.0010 0.0003
## 280 1.1037 nan 0.0010 0.0003
## 300 1.0915 nan 0.0010 0.0003
## 320 1.0796 nan 0.0010 0.0002
## 340 1.0682 nan 0.0010 0.0003
## 360 1.0569 nan 0.0010 0.0003
## 380 1.0462 nan 0.0010 0.0002
## 400 1.0358 nan 0.0010 0.0002
## 420 1.0254 nan 0.0010 0.0002
## 440 1.0154 nan 0.0010 0.0002
## 460 1.0057 nan 0.0010 0.0002
## 480 0.9962 nan 0.0010 0.0002
## 500 0.9872 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0005
## 20 1.3016 nan 0.0010 0.0004
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2479 nan 0.0010 0.0003
## 100 1.2314 nan 0.0010 0.0004
## 120 1.2156 nan 0.0010 0.0003
## 140 1.2000 nan 0.0010 0.0003
## 160 1.1849 nan 0.0010 0.0004
## 180 1.1702 nan 0.0010 0.0003
## 200 1.1564 nan 0.0010 0.0003
## 220 1.1429 nan 0.0010 0.0003
## 240 1.1298 nan 0.0010 0.0003
## 260 1.1171 nan 0.0010 0.0003
## 280 1.1048 nan 0.0010 0.0002
## 300 1.0926 nan 0.0010 0.0003
## 320 1.0810 nan 0.0010 0.0003
## 340 1.0695 nan 0.0010 0.0002
## 360 1.0585 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0002
## 400 1.0376 nan 0.0010 0.0002
## 420 1.0273 nan 0.0010 0.0002
## 440 1.0175 nan 0.0010 0.0002
## 460 1.0076 nan 0.0010 0.0002
## 480 0.9983 nan 0.0010 0.0002
## 500 0.9891 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0005
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2663 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0004
## 100 1.2329 nan 0.0010 0.0004
## 120 1.2170 nan 0.0010 0.0004
## 140 1.2017 nan 0.0010 0.0003
## 160 1.1868 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1584 nan 0.0010 0.0003
## 220 1.1449 nan 0.0010 0.0003
## 240 1.1316 nan 0.0010 0.0003
## 260 1.1188 nan 0.0010 0.0003
## 280 1.1062 nan 0.0010 0.0003
## 300 1.0944 nan 0.0010 0.0002
## 320 1.0827 nan 0.0010 0.0002
## 340 1.0716 nan 0.0010 0.0003
## 360 1.0606 nan 0.0010 0.0002
## 380 1.0499 nan 0.0010 0.0002
## 400 1.0397 nan 0.0010 0.0002
## 420 1.0297 nan 0.0010 0.0003
## 440 1.0201 nan 0.0010 0.0002
## 460 1.0107 nan 0.0010 0.0002
## 480 1.0012 nan 0.0010 0.0002
## 500 0.9921 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0037
## 2 1.3028 nan 0.0100 0.0042
## 3 1.2944 nan 0.0100 0.0037
## 4 1.2863 nan 0.0100 0.0039
## 5 1.2785 nan 0.0100 0.0035
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2620 nan 0.0100 0.0034
## 8 1.2548 nan 0.0100 0.0029
## 9 1.2475 nan 0.0100 0.0030
## 10 1.2408 nan 0.0100 0.0030
## 20 1.1745 nan 0.0100 0.0027
## 40 1.0667 nan 0.0100 0.0020
## 60 0.9843 nan 0.0100 0.0015
## 80 0.9185 nan 0.0100 0.0011
## 100 0.8660 nan 0.0100 0.0010
## 120 0.8224 nan 0.0100 0.0007
## 140 0.7855 nan 0.0100 0.0006
## 160 0.7553 nan 0.0100 0.0006
## 180 0.7299 nan 0.0100 0.0004
## 200 0.7062 nan 0.0100 0.0002
## 220 0.6859 nan 0.0100 0.0003
## 240 0.6689 nan 0.0100 0.0002
## 260 0.6536 nan 0.0100 0.0001
## 280 0.6375 nan 0.0100 0.0002
## 300 0.6244 nan 0.0100 0.0001
## 320 0.6112 nan 0.0100 0.0002
## 340 0.6002 nan 0.0100 0.0001
## 360 0.5892 nan 0.0100 0.0001
## 380 0.5788 nan 0.0100 0.0001
## 400 0.5693 nan 0.0100 -0.0000
## 420 0.5600 nan 0.0100 0.0001
## 440 0.5509 nan 0.0100 0.0000
## 460 0.5417 nan 0.0100 0.0001
## 480 0.5334 nan 0.0100 -0.0000
## 500 0.5253 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0042
## 2 1.3033 nan 0.0100 0.0037
## 3 1.2952 nan 0.0100 0.0035
## 4 1.2875 nan 0.0100 0.0035
## 5 1.2792 nan 0.0100 0.0035
## 6 1.2712 nan 0.0100 0.0037
## 7 1.2641 nan 0.0100 0.0032
## 8 1.2571 nan 0.0100 0.0029
## 9 1.2495 nan 0.0100 0.0034
## 10 1.2417 nan 0.0100 0.0036
## 20 1.1743 nan 0.0100 0.0026
## 40 1.0650 nan 0.0100 0.0021
## 60 0.9829 nan 0.0100 0.0012
## 80 0.9197 nan 0.0100 0.0009
## 100 0.8659 nan 0.0100 0.0010
## 120 0.8225 nan 0.0100 0.0007
## 140 0.7873 nan 0.0100 0.0004
## 160 0.7575 nan 0.0100 0.0004
## 180 0.7333 nan 0.0100 0.0003
## 200 0.7117 nan 0.0100 -0.0000
## 220 0.6912 nan 0.0100 0.0002
## 240 0.6743 nan 0.0100 0.0001
## 260 0.6600 nan 0.0100 0.0000
## 280 0.6456 nan 0.0100 -0.0001
## 300 0.6329 nan 0.0100 0.0001
## 320 0.6208 nan 0.0100 -0.0002
## 340 0.6086 nan 0.0100 0.0002
## 360 0.5982 nan 0.0100 0.0001
## 380 0.5875 nan 0.0100 0.0000
## 400 0.5780 nan 0.0100 0.0000
## 420 0.5690 nan 0.0100 -0.0001
## 440 0.5600 nan 0.0100 -0.0001
## 460 0.5510 nan 0.0100 0.0001
## 480 0.5426 nan 0.0100 -0.0000
## 500 0.5338 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0042
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2954 nan 0.0100 0.0038
## 4 1.2878 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0039
## 6 1.2711 nan 0.0100 0.0036
## 7 1.2630 nan 0.0100 0.0038
## 8 1.2549 nan 0.0100 0.0034
## 9 1.2475 nan 0.0100 0.0035
## 10 1.2399 nan 0.0100 0.0031
## 20 1.1725 nan 0.0100 0.0029
## 40 1.0655 nan 0.0100 0.0020
## 60 0.9843 nan 0.0100 0.0016
## 80 0.9197 nan 0.0100 0.0012
## 100 0.8686 nan 0.0100 0.0008
## 120 0.8266 nan 0.0100 0.0004
## 140 0.7905 nan 0.0100 0.0003
## 160 0.7605 nan 0.0100 0.0005
## 180 0.7356 nan 0.0100 0.0002
## 200 0.7132 nan 0.0100 0.0003
## 220 0.6939 nan 0.0100 0.0001
## 240 0.6779 nan 0.0100 0.0003
## 260 0.6624 nan 0.0100 -0.0000
## 280 0.6484 nan 0.0100 0.0000
## 300 0.6356 nan 0.0100 0.0002
## 320 0.6237 nan 0.0100 0.0000
## 340 0.6121 nan 0.0100 0.0000
## 360 0.6012 nan 0.0100 -0.0000
## 380 0.5911 nan 0.0100 0.0001
## 400 0.5818 nan 0.0100 -0.0001
## 420 0.5733 nan 0.0100 -0.0000
## 440 0.5644 nan 0.0100 -0.0000
## 460 0.5566 nan 0.0100 -0.0000
## 480 0.5482 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0041
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2959 nan 0.0100 0.0039
## 4 1.2874 nan 0.0100 0.0041
## 5 1.2793 nan 0.0100 0.0036
## 6 1.2701 nan 0.0100 0.0043
## 7 1.2630 nan 0.0100 0.0034
## 8 1.2545 nan 0.0100 0.0035
## 9 1.2469 nan 0.0100 0.0033
## 10 1.2384 nan 0.0100 0.0039
## 20 1.1639 nan 0.0100 0.0028
## 40 1.0483 nan 0.0100 0.0024
## 60 0.9616 nan 0.0100 0.0013
## 80 0.8919 nan 0.0100 0.0012
## 100 0.8371 nan 0.0100 0.0007
## 120 0.7926 nan 0.0100 0.0007
## 140 0.7541 nan 0.0100 0.0005
## 160 0.7217 nan 0.0100 0.0001
## 180 0.6935 nan 0.0100 0.0005
## 200 0.6706 nan 0.0100 0.0004
## 220 0.6496 nan 0.0100 0.0002
## 240 0.6303 nan 0.0100 0.0002
## 260 0.6126 nan 0.0100 0.0002
## 280 0.5961 nan 0.0100 0.0001
## 300 0.5813 nan 0.0100 0.0001
## 320 0.5673 nan 0.0100 -0.0001
## 340 0.5549 nan 0.0100 -0.0001
## 360 0.5426 nan 0.0100 0.0002
## 380 0.5307 nan 0.0100 0.0001
## 400 0.5198 nan 0.0100 -0.0003
## 420 0.5087 nan 0.0100 -0.0000
## 440 0.4978 nan 0.0100 -0.0000
## 460 0.4878 nan 0.0100 0.0001
## 480 0.4789 nan 0.0100 -0.0002
## 500 0.4699 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3028 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2851 nan 0.0100 0.0036
## 5 1.2762 nan 0.0100 0.0038
## 6 1.2686 nan 0.0100 0.0034
## 7 1.2605 nan 0.0100 0.0034
## 8 1.2525 nan 0.0100 0.0032
## 9 1.2448 nan 0.0100 0.0035
## 10 1.2366 nan 0.0100 0.0038
## 20 1.1634 nan 0.0100 0.0034
## 40 1.0482 nan 0.0100 0.0023
## 60 0.9630 nan 0.0100 0.0015
## 80 0.8936 nan 0.0100 0.0011
## 100 0.8385 nan 0.0100 0.0011
## 120 0.7945 nan 0.0100 0.0008
## 140 0.7578 nan 0.0100 0.0004
## 160 0.7272 nan 0.0100 0.0007
## 180 0.7007 nan 0.0100 0.0002
## 200 0.6779 nan 0.0100 0.0001
## 220 0.6553 nan 0.0100 0.0004
## 240 0.6357 nan 0.0100 0.0003
## 260 0.6189 nan 0.0100 0.0002
## 280 0.6036 nan 0.0100 0.0000
## 300 0.5904 nan 0.0100 0.0002
## 320 0.5770 nan 0.0100 0.0001
## 340 0.5644 nan 0.0100 0.0000
## 360 0.5534 nan 0.0100 0.0001
## 380 0.5424 nan 0.0100 0.0000
## 400 0.5323 nan 0.0100 -0.0001
## 420 0.5219 nan 0.0100 -0.0000
## 440 0.5120 nan 0.0100 0.0000
## 460 0.5016 nan 0.0100 -0.0001
## 480 0.4926 nan 0.0100 -0.0000
## 500 0.4835 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0040
## 3 1.2938 nan 0.0100 0.0041
## 4 1.2853 nan 0.0100 0.0035
## 5 1.2761 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0039
## 7 1.2598 nan 0.0100 0.0041
## 8 1.2534 nan 0.0100 0.0025
## 9 1.2453 nan 0.0100 0.0035
## 10 1.2375 nan 0.0100 0.0034
## 20 1.1671 nan 0.0100 0.0032
## 40 1.0523 nan 0.0100 0.0022
## 60 0.9657 nan 0.0100 0.0018
## 80 0.8975 nan 0.0100 0.0016
## 100 0.8427 nan 0.0100 0.0010
## 120 0.7978 nan 0.0100 0.0007
## 140 0.7609 nan 0.0100 0.0003
## 160 0.7308 nan 0.0100 0.0004
## 180 0.7046 nan 0.0100 0.0002
## 200 0.6823 nan 0.0100 0.0001
## 220 0.6624 nan 0.0100 0.0002
## 240 0.6449 nan 0.0100 0.0002
## 260 0.6284 nan 0.0100 -0.0001
## 280 0.6141 nan 0.0100 -0.0000
## 300 0.5997 nan 0.0100 -0.0000
## 320 0.5862 nan 0.0100 0.0000
## 340 0.5742 nan 0.0100 0.0000
## 360 0.5625 nan 0.0100 0.0000
## 380 0.5505 nan 0.0100 0.0001
## 400 0.5395 nan 0.0100 0.0000
## 420 0.5289 nan 0.0100 0.0000
## 440 0.5200 nan 0.0100 0.0001
## 460 0.5110 nan 0.0100 -0.0002
## 480 0.5013 nan 0.0100 -0.0000
## 500 0.4928 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0044
## 2 1.3015 nan 0.0100 0.0042
## 3 1.2925 nan 0.0100 0.0042
## 4 1.2834 nan 0.0100 0.0038
## 5 1.2741 nan 0.0100 0.0042
## 6 1.2650 nan 0.0100 0.0038
## 7 1.2571 nan 0.0100 0.0035
## 8 1.2481 nan 0.0100 0.0040
## 9 1.2393 nan 0.0100 0.0041
## 10 1.2304 nan 0.0100 0.0039
## 20 1.1535 nan 0.0100 0.0029
## 40 1.0320 nan 0.0100 0.0023
## 60 0.9427 nan 0.0100 0.0017
## 80 0.8725 nan 0.0100 0.0011
## 100 0.8125 nan 0.0100 0.0009
## 120 0.7670 nan 0.0100 0.0007
## 140 0.7291 nan 0.0100 0.0006
## 160 0.6942 nan 0.0100 0.0003
## 180 0.6655 nan 0.0100 0.0003
## 200 0.6399 nan 0.0100 0.0002
## 220 0.6175 nan 0.0100 0.0002
## 240 0.5989 nan 0.0100 0.0001
## 260 0.5805 nan 0.0100 0.0003
## 280 0.5633 nan 0.0100 0.0000
## 300 0.5475 nan 0.0100 0.0001
## 320 0.5330 nan 0.0100 0.0002
## 340 0.5195 nan 0.0100 -0.0001
## 360 0.5062 nan 0.0100 0.0000
## 380 0.4934 nan 0.0100 -0.0000
## 400 0.4815 nan 0.0100 0.0000
## 420 0.4701 nan 0.0100 0.0001
## 440 0.4591 nan 0.0100 -0.0000
## 460 0.4484 nan 0.0100 0.0000
## 480 0.4386 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3024 nan 0.0100 0.0038
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2850 nan 0.0100 0.0043
## 5 1.2768 nan 0.0100 0.0038
## 6 1.2684 nan 0.0100 0.0039
## 7 1.2601 nan 0.0100 0.0034
## 8 1.2517 nan 0.0100 0.0037
## 9 1.2429 nan 0.0100 0.0039
## 10 1.2359 nan 0.0100 0.0031
## 20 1.1588 nan 0.0100 0.0031
## 40 1.0375 nan 0.0100 0.0023
## 60 0.9466 nan 0.0100 0.0018
## 80 0.8756 nan 0.0100 0.0013
## 100 0.8188 nan 0.0100 0.0011
## 120 0.7723 nan 0.0100 0.0006
## 140 0.7322 nan 0.0100 0.0005
## 160 0.6997 nan 0.0100 0.0001
## 180 0.6712 nan 0.0100 0.0006
## 200 0.6475 nan 0.0100 -0.0000
## 220 0.6262 nan 0.0100 0.0003
## 240 0.6063 nan 0.0100 0.0001
## 260 0.5877 nan 0.0100 0.0000
## 280 0.5707 nan 0.0100 -0.0000
## 300 0.5546 nan 0.0100 0.0002
## 320 0.5404 nan 0.0100 0.0000
## 340 0.5263 nan 0.0100 -0.0000
## 360 0.5134 nan 0.0100 -0.0002
## 380 0.5004 nan 0.0100 0.0001
## 400 0.4891 nan 0.0100 -0.0000
## 420 0.4783 nan 0.0100 0.0000
## 440 0.4670 nan 0.0100 -0.0001
## 460 0.4569 nan 0.0100 -0.0000
## 480 0.4470 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0037
## 4 1.2850 nan 0.0100 0.0042
## 5 1.2766 nan 0.0100 0.0035
## 6 1.2673 nan 0.0100 0.0041
## 7 1.2584 nan 0.0100 0.0038
## 8 1.2503 nan 0.0100 0.0037
## 9 1.2416 nan 0.0100 0.0040
## 10 1.2333 nan 0.0100 0.0035
## 20 1.1588 nan 0.0100 0.0025
## 40 1.0415 nan 0.0100 0.0022
## 60 0.9506 nan 0.0100 0.0013
## 80 0.8795 nan 0.0100 0.0013
## 100 0.8227 nan 0.0100 0.0009
## 120 0.7769 nan 0.0100 0.0006
## 140 0.7405 nan 0.0100 0.0004
## 160 0.7085 nan 0.0100 0.0003
## 180 0.6803 nan 0.0100 0.0003
## 200 0.6557 nan 0.0100 0.0004
## 220 0.6354 nan 0.0100 0.0001
## 240 0.6158 nan 0.0100 -0.0001
## 260 0.5976 nan 0.0100 0.0000
## 280 0.5815 nan 0.0100 -0.0000
## 300 0.5665 nan 0.0100 0.0000
## 320 0.5530 nan 0.0100 0.0001
## 340 0.5387 nan 0.0100 0.0000
## 360 0.5250 nan 0.0100 0.0001
## 380 0.5142 nan 0.0100 -0.0001
## 400 0.5030 nan 0.0100 -0.0000
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## 440 0.4821 nan 0.0100 -0.0001
## 460 0.4723 nan 0.0100 0.0001
## 480 0.4622 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2390 nan 0.1000 0.0380
## 2 1.1665 nan 0.1000 0.0319
## 3 1.1073 nan 0.1000 0.0276
## 4 1.0578 nan 0.1000 0.0233
## 5 1.0142 nan 0.1000 0.0174
## 6 0.9747 nan 0.1000 0.0176
## 7 0.9387 nan 0.1000 0.0154
## 8 0.9123 nan 0.1000 0.0097
## 9 0.8800 nan 0.1000 0.0125
## 10 0.8553 nan 0.1000 0.0080
## 20 0.7077 nan 0.1000 0.0027
## 40 0.5731 nan 0.1000 0.0009
## 60 0.4900 nan 0.1000 0.0001
## 80 0.4305 nan 0.1000 -0.0009
## 100 0.3739 nan 0.1000 -0.0001
## 120 0.3309 nan 0.1000 -0.0004
## 140 0.2939 nan 0.1000 -0.0008
## 160 0.2636 nan 0.1000 -0.0009
## 180 0.2338 nan 0.1000 -0.0000
## 200 0.2118 nan 0.1000 -0.0008
## 220 0.1925 nan 0.1000 -0.0008
## 240 0.1745 nan 0.1000 -0.0002
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## 280 0.1469 nan 0.1000 -0.0003
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## 320 0.1216 nan 0.1000 -0.0005
## 340 0.1108 nan 0.1000 -0.0002
## 360 0.1004 nan 0.1000 -0.0001
## 380 0.0921 nan 0.1000 -0.0004
## 400 0.0853 nan 0.1000 -0.0003
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## 460 0.0666 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0412
## 2 1.1689 nan 0.1000 0.0320
## 3 1.1105 nan 0.1000 0.0250
## 4 1.0586 nan 0.1000 0.0243
## 5 1.0181 nan 0.1000 0.0192
## 6 0.9777 nan 0.1000 0.0170
## 7 0.9444 nan 0.1000 0.0135
## 8 0.9154 nan 0.1000 0.0115
## 9 0.8888 nan 0.1000 0.0108
## 10 0.8663 nan 0.1000 0.0094
## 20 0.7118 nan 0.1000 0.0033
## 40 0.5855 nan 0.1000 -0.0007
## 60 0.4978 nan 0.1000 0.0006
## 80 0.4275 nan 0.1000 -0.0014
## 100 0.3774 nan 0.1000 -0.0004
## 120 0.3343 nan 0.1000 -0.0009
## 140 0.3007 nan 0.1000 -0.0009
## 160 0.2708 nan 0.1000 -0.0003
## 180 0.2468 nan 0.1000 -0.0004
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## 360 0.1079 nan 0.1000 -0.0002
## 380 0.0992 nan 0.1000 -0.0002
## 400 0.0920 nan 0.1000 -0.0003
## 420 0.0838 nan 0.1000 -0.0003
## 440 0.0771 nan 0.1000 -0.0001
## 460 0.0704 nan 0.1000 -0.0002
## 480 0.0651 nan 0.1000 -0.0001
## 500 0.0603 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0333
## 2 1.1719 nan 0.1000 0.0303
## 3 1.1162 nan 0.1000 0.0259
## 4 1.0687 nan 0.1000 0.0211
## 5 1.0250 nan 0.1000 0.0177
## 6 0.9855 nan 0.1000 0.0155
## 7 0.9543 nan 0.1000 0.0133
## 8 0.9224 nan 0.1000 0.0141
## 9 0.8965 nan 0.1000 0.0093
## 10 0.8699 nan 0.1000 0.0072
## 20 0.7147 nan 0.1000 0.0016
## 40 0.5858 nan 0.1000 -0.0009
## 60 0.5076 nan 0.1000 0.0007
## 80 0.4488 nan 0.1000 -0.0008
## 100 0.4018 nan 0.1000 -0.0015
## 120 0.3611 nan 0.1000 -0.0001
## 140 0.3204 nan 0.1000 -0.0006
## 160 0.2904 nan 0.1000 -0.0005
## 180 0.2645 nan 0.1000 -0.0003
## 200 0.2405 nan 0.1000 -0.0007
## 220 0.2211 nan 0.1000 -0.0009
## 240 0.2000 nan 0.1000 -0.0006
## 260 0.1821 nan 0.1000 -0.0004
## 280 0.1678 nan 0.1000 -0.0003
## 300 0.1559 nan 0.1000 -0.0007
## 320 0.1430 nan 0.1000 -0.0003
## 340 0.1316 nan 0.1000 -0.0004
## 360 0.1214 nan 0.1000 -0.0006
## 380 0.1110 nan 0.1000 -0.0003
## 400 0.1032 nan 0.1000 -0.0006
## 420 0.0954 nan 0.1000 -0.0001
## 440 0.0884 nan 0.1000 -0.0001
## 460 0.0818 nan 0.1000 -0.0002
## 480 0.0755 nan 0.1000 -0.0001
## 500 0.0691 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0430
## 2 1.1588 nan 0.1000 0.0289
## 3 1.0998 nan 0.1000 0.0236
## 4 1.0497 nan 0.1000 0.0222
## 5 1.0015 nan 0.1000 0.0186
## 6 0.9591 nan 0.1000 0.0180
## 7 0.9186 nan 0.1000 0.0154
## 8 0.8886 nan 0.1000 0.0107
## 9 0.8656 nan 0.1000 0.0094
## 10 0.8408 nan 0.1000 0.0088
## 20 0.6858 nan 0.1000 -0.0001
## 40 0.5346 nan 0.1000 0.0001
## 60 0.4391 nan 0.1000 -0.0006
## 80 0.3686 nan 0.1000 -0.0012
## 100 0.3193 nan 0.1000 -0.0000
## 120 0.2781 nan 0.1000 -0.0006
## 140 0.2422 nan 0.1000 -0.0003
## 160 0.2120 nan 0.1000 -0.0009
## 180 0.1864 nan 0.1000 -0.0003
## 200 0.1643 nan 0.1000 0.0002
## 220 0.1464 nan 0.1000 -0.0002
## 240 0.1292 nan 0.1000 -0.0000
## 260 0.1153 nan 0.1000 -0.0000
## 280 0.1020 nan 0.1000 -0.0003
## 300 0.0910 nan 0.1000 -0.0003
## 320 0.0813 nan 0.1000 -0.0003
## 340 0.0729 nan 0.1000 -0.0001
## 360 0.0660 nan 0.1000 -0.0000
## 380 0.0593 nan 0.1000 -0.0001
## 400 0.0538 nan 0.1000 -0.0002
## 420 0.0490 nan 0.1000 -0.0002
## 440 0.0442 nan 0.1000 -0.0000
## 460 0.0402 nan 0.1000 -0.0001
## 480 0.0363 nan 0.1000 -0.0002
## 500 0.0331 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0386
## 2 1.1734 nan 0.1000 0.0307
## 3 1.1095 nan 0.1000 0.0257
## 4 1.0490 nan 0.1000 0.0246
## 5 1.0059 nan 0.1000 0.0176
## 6 0.9661 nan 0.1000 0.0159
## 7 0.9243 nan 0.1000 0.0182
## 8 0.8936 nan 0.1000 0.0117
## 9 0.8665 nan 0.1000 0.0107
## 10 0.8453 nan 0.1000 0.0079
## 20 0.6894 nan 0.1000 0.0037
## 40 0.5419 nan 0.1000 0.0010
## 60 0.4525 nan 0.1000 -0.0012
## 80 0.3904 nan 0.1000 -0.0015
## 100 0.3337 nan 0.1000 -0.0006
## 120 0.2854 nan 0.1000 -0.0010
## 140 0.2528 nan 0.1000 -0.0008
## 160 0.2218 nan 0.1000 -0.0001
## 180 0.1954 nan 0.1000 -0.0011
## 200 0.1702 nan 0.1000 -0.0003
## 220 0.1499 nan 0.1000 -0.0004
## 240 0.1330 nan 0.1000 -0.0005
## 260 0.1181 nan 0.1000 -0.0002
## 280 0.1062 nan 0.1000 -0.0004
## 300 0.0939 nan 0.1000 -0.0001
## 320 0.0844 nan 0.1000 -0.0001
## 340 0.0757 nan 0.1000 -0.0001
## 360 0.0682 nan 0.1000 -0.0001
## 380 0.0613 nan 0.1000 -0.0001
## 400 0.0554 nan 0.1000 -0.0002
## 420 0.0487 nan 0.1000 0.0000
## 440 0.0435 nan 0.1000 -0.0002
## 460 0.0398 nan 0.1000 -0.0001
## 480 0.0358 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2307 nan 0.1000 0.0411
## 2 1.1535 nan 0.1000 0.0329
## 3 1.0868 nan 0.1000 0.0313
## 4 1.0425 nan 0.1000 0.0175
## 5 0.9927 nan 0.1000 0.0222
## 6 0.9541 nan 0.1000 0.0155
## 7 0.9208 nan 0.1000 0.0111
## 8 0.8895 nan 0.1000 0.0102
## 9 0.8605 nan 0.1000 0.0114
## 10 0.8381 nan 0.1000 0.0080
## 20 0.6784 nan 0.1000 0.0005
## 40 0.5425 nan 0.1000 -0.0001
## 60 0.4600 nan 0.1000 -0.0002
## 80 0.3838 nan 0.1000 -0.0009
## 100 0.3312 nan 0.1000 -0.0002
## 120 0.2857 nan 0.1000 -0.0006
## 140 0.2531 nan 0.1000 -0.0006
## 160 0.2224 nan 0.1000 -0.0011
## 180 0.1965 nan 0.1000 -0.0001
## 200 0.1739 nan 0.1000 -0.0004
## 220 0.1561 nan 0.1000 -0.0006
## 240 0.1391 nan 0.1000 -0.0009
## 260 0.1247 nan 0.1000 -0.0004
## 280 0.1112 nan 0.1000 -0.0004
## 300 0.1004 nan 0.1000 -0.0002
## 320 0.0897 nan 0.1000 -0.0003
## 340 0.0798 nan 0.1000 -0.0001
## 360 0.0722 nan 0.1000 -0.0002
## 380 0.0655 nan 0.1000 -0.0001
## 400 0.0595 nan 0.1000 -0.0002
## 420 0.0539 nan 0.1000 -0.0002
## 440 0.0487 nan 0.1000 -0.0002
## 460 0.0446 nan 0.1000 -0.0000
## 480 0.0401 nan 0.1000 -0.0001
## 500 0.0364 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.1000 0.0421
## 2 1.1499 nan 0.1000 0.0300
## 3 1.0932 nan 0.1000 0.0243
## 4 1.0371 nan 0.1000 0.0266
## 5 0.9873 nan 0.1000 0.0198
## 6 0.9508 nan 0.1000 0.0134
## 7 0.9084 nan 0.1000 0.0179
## 8 0.8696 nan 0.1000 0.0148
## 9 0.8389 nan 0.1000 0.0116
## 10 0.8166 nan 0.1000 0.0085
## 20 0.6429 nan 0.1000 -0.0003
## 40 0.4837 nan 0.1000 -0.0001
## 60 0.3937 nan 0.1000 -0.0002
## 80 0.3186 nan 0.1000 -0.0012
## 100 0.2702 nan 0.1000 -0.0006
## 120 0.2227 nan 0.1000 -0.0004
## 140 0.1881 nan 0.1000 -0.0003
## 160 0.1589 nan 0.1000 -0.0000
## 180 0.1381 nan 0.1000 -0.0004
## 200 0.1211 nan 0.1000 -0.0001
## 220 0.1044 nan 0.1000 -0.0004
## 240 0.0914 nan 0.1000 -0.0002
## 260 0.0797 nan 0.1000 -0.0000
## 280 0.0697 nan 0.1000 -0.0002
## 300 0.0613 nan 0.1000 -0.0001
## 320 0.0542 nan 0.1000 -0.0000
## 340 0.0479 nan 0.1000 0.0000
## 360 0.0428 nan 0.1000 -0.0001
## 380 0.0379 nan 0.1000 -0.0000
## 400 0.0335 nan 0.1000 -0.0000
## 420 0.0296 nan 0.1000 0.0000
## 440 0.0261 nan 0.1000 -0.0001
## 460 0.0231 nan 0.1000 -0.0000
## 480 0.0203 nan 0.1000 -0.0000
## 500 0.0183 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2195 nan 0.1000 0.0448
## 2 1.1484 nan 0.1000 0.0369
## 3 1.0880 nan 0.1000 0.0233
## 4 1.0278 nan 0.1000 0.0232
## 5 0.9807 nan 0.1000 0.0216
## 6 0.9381 nan 0.1000 0.0171
## 7 0.9073 nan 0.1000 0.0133
## 8 0.8744 nan 0.1000 0.0123
## 9 0.8469 nan 0.1000 0.0114
## 10 0.8215 nan 0.1000 0.0106
## 20 0.6548 nan 0.1000 0.0032
## 40 0.4957 nan 0.1000 -0.0002
## 60 0.4047 nan 0.1000 -0.0011
## 80 0.3350 nan 0.1000 -0.0006
## 100 0.2772 nan 0.1000 0.0004
## 120 0.2322 nan 0.1000 -0.0001
## 140 0.1944 nan 0.1000 -0.0011
## 160 0.1683 nan 0.1000 -0.0000
## 180 0.1438 nan 0.1000 -0.0005
## 200 0.1232 nan 0.1000 -0.0004
## 220 0.1083 nan 0.1000 -0.0006
## 240 0.0947 nan 0.1000 -0.0001
## 260 0.0823 nan 0.1000 -0.0003
## 280 0.0716 nan 0.1000 -0.0002
## 300 0.0622 nan 0.1000 -0.0002
## 320 0.0546 nan 0.1000 -0.0000
## 340 0.0479 nan 0.1000 -0.0001
## 360 0.0420 nan 0.1000 -0.0002
## 380 0.0369 nan 0.1000 -0.0002
## 400 0.0326 nan 0.1000 -0.0001
## 420 0.0289 nan 0.1000 -0.0000
## 440 0.0255 nan 0.1000 -0.0001
## 460 0.0229 nan 0.1000 -0.0001
## 480 0.0202 nan 0.1000 -0.0000
## 500 0.0180 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2224 nan 0.1000 0.0457
## 2 1.1503 nan 0.1000 0.0303
## 3 1.0878 nan 0.1000 0.0276
## 4 1.0377 nan 0.1000 0.0234
## 5 0.9947 nan 0.1000 0.0189
## 6 0.9563 nan 0.1000 0.0163
## 7 0.9160 nan 0.1000 0.0170
## 8 0.8867 nan 0.1000 0.0112
## 9 0.8554 nan 0.1000 0.0125
## 10 0.8264 nan 0.1000 0.0111
## 20 0.6587 nan 0.1000 0.0023
## 40 0.5114 nan 0.1000 -0.0014
## 60 0.4074 nan 0.1000 -0.0007
## 80 0.3376 nan 0.1000 0.0001
## 100 0.2861 nan 0.1000 -0.0009
## 120 0.2463 nan 0.1000 -0.0012
## 140 0.2117 nan 0.1000 -0.0007
## 160 0.1808 nan 0.1000 -0.0009
## 180 0.1555 nan 0.1000 -0.0004
## 200 0.1339 nan 0.1000 -0.0006
## 220 0.1147 nan 0.1000 -0.0002
## 240 0.0991 nan 0.1000 -0.0006
## 260 0.0877 nan 0.1000 -0.0003
## 280 0.0766 nan 0.1000 -0.0003
## 300 0.0670 nan 0.1000 -0.0001
## 320 0.0583 nan 0.1000 -0.0001
## 340 0.0518 nan 0.1000 -0.0002
## 360 0.0463 nan 0.1000 -0.0002
## 380 0.0407 nan 0.1000 -0.0000
## 400 0.0361 nan 0.1000 -0.0002
## 420 0.0318 nan 0.1000 -0.0001
## 440 0.0281 nan 0.1000 -0.0000
## 460 0.0251 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0002
## 500 0.0198 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2723 nan 0.0010 0.0004
## 80 1.2574 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2288 nan 0.0010 0.0003
## 140 1.2153 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1895 nan 0.0010 0.0002
## 200 1.1769 nan 0.0010 0.0003
## 220 1.1651 nan 0.0010 0.0002
## 240 1.1536 nan 0.0010 0.0002
## 260 1.1422 nan 0.0010 0.0003
## 280 1.1312 nan 0.0010 0.0003
## 300 1.1208 nan 0.0010 0.0003
## 320 1.1103 nan 0.0010 0.0002
## 340 1.1001 nan 0.0010 0.0002
## 360 1.0904 nan 0.0010 0.0002
## 380 1.0807 nan 0.0010 0.0002
## 400 1.0717 nan 0.0010 0.0002
## 420 1.0627 nan 0.0010 0.0002
## 440 1.0539 nan 0.0010 0.0002
## 460 1.0455 nan 0.0010 0.0002
## 480 1.0371 nan 0.0010 0.0002
## 500 1.0289 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0004
## 60 1.2720 nan 0.0010 0.0003
## 80 1.2569 nan 0.0010 0.0004
## 100 1.2426 nan 0.0010 0.0004
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2148 nan 0.0010 0.0003
## 160 1.2016 nan 0.0010 0.0002
## 180 1.1891 nan 0.0010 0.0003
## 200 1.1767 nan 0.0010 0.0003
## 220 1.1650 nan 0.0010 0.0002
## 240 1.1533 nan 0.0010 0.0003
## 260 1.1418 nan 0.0010 0.0002
## 280 1.1310 nan 0.0010 0.0002
## 300 1.1202 nan 0.0010 0.0003
## 320 1.1099 nan 0.0010 0.0002
## 340 1.1000 nan 0.0010 0.0002
## 360 1.0903 nan 0.0010 0.0002
## 380 1.0807 nan 0.0010 0.0002
## 400 1.0718 nan 0.0010 0.0002
## 420 1.0629 nan 0.0010 0.0002
## 440 1.0541 nan 0.0010 0.0002
## 460 1.0456 nan 0.0010 0.0002
## 480 1.0373 nan 0.0010 0.0002
## 500 1.0292 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0003
## 80 1.2581 nan 0.0010 0.0004
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2293 nan 0.0010 0.0003
## 140 1.2159 nan 0.0010 0.0003
## 160 1.2027 nan 0.0010 0.0003
## 180 1.1898 nan 0.0010 0.0003
## 200 1.1779 nan 0.0010 0.0002
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1547 nan 0.0010 0.0002
## 260 1.1435 nan 0.0010 0.0003
## 280 1.1327 nan 0.0010 0.0002
## 300 1.1219 nan 0.0010 0.0002
## 320 1.1117 nan 0.0010 0.0002
## 340 1.1015 nan 0.0010 0.0002
## 360 1.0918 nan 0.0010 0.0002
## 380 1.0825 nan 0.0010 0.0002
## 400 1.0735 nan 0.0010 0.0002
## 420 1.0646 nan 0.0010 0.0002
## 440 1.0559 nan 0.0010 0.0002
## 460 1.0474 nan 0.0010 0.0002
## 480 1.0392 nan 0.0010 0.0001
## 500 1.0313 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2528 nan 0.0010 0.0003
## 100 1.2375 nan 0.0010 0.0004
## 120 1.2227 nan 0.0010 0.0003
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1805 nan 0.0010 0.0003
## 200 1.1672 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0003
## 240 1.1420 nan 0.0010 0.0003
## 260 1.1299 nan 0.0010 0.0002
## 280 1.1185 nan 0.0010 0.0003
## 300 1.1072 nan 0.0010 0.0003
## 320 1.0963 nan 0.0010 0.0002
## 340 1.0857 nan 0.0010 0.0002
## 360 1.0752 nan 0.0010 0.0002
## 380 1.0654 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0461 nan 0.0010 0.0002
## 440 1.0371 nan 0.0010 0.0002
## 460 1.0280 nan 0.0010 0.0002
## 480 1.0195 nan 0.0010 0.0002
## 500 1.0111 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0003
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0003
## 100 1.2387 nan 0.0010 0.0003
## 120 1.2237 nan 0.0010 0.0003
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1958 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1566 nan 0.0010 0.0003
## 240 1.1445 nan 0.0010 0.0002
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1211 nan 0.0010 0.0002
## 300 1.1098 nan 0.0010 0.0002
## 320 1.0989 nan 0.0010 0.0002
## 340 1.0885 nan 0.0010 0.0002
## 360 1.0781 nan 0.0010 0.0002
## 380 1.0682 nan 0.0010 0.0002
## 400 1.0584 nan 0.0010 0.0002
## 420 1.0490 nan 0.0010 0.0002
## 440 1.0399 nan 0.0010 0.0002
## 460 1.0309 nan 0.0010 0.0002
## 480 1.0222 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2546 nan 0.0010 0.0004
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2248 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
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## 300 1.1105 nan 0.0010 0.0003
## 320 1.0999 nan 0.0010 0.0002
## 340 1.0894 nan 0.0010 0.0002
## 360 1.0794 nan 0.0010 0.0002
## 380 1.0694 nan 0.0010 0.0002
## 400 1.0598 nan 0.0010 0.0002
## 420 1.0505 nan 0.0010 0.0002
## 440 1.0414 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0242 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2342 nan 0.0010 0.0003
## 120 1.2187 nan 0.0010 0.0004
## 140 1.2038 nan 0.0010 0.0003
## 160 1.1894 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
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## 300 1.0992 nan 0.0010 0.0002
## 320 1.0879 nan 0.0010 0.0003
## 340 1.0768 nan 0.0010 0.0002
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## 380 1.0549 nan 0.0010 0.0002
## 400 1.0445 nan 0.0010 0.0002
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## 440 1.0251 nan 0.0010 0.0001
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## 480 1.0067 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0005
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## 10 1.3118 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0004
## 80 1.2502 nan 0.0010 0.0004
## 100 1.2339 nan 0.0010 0.0003
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## 140 1.2036 nan 0.0010 0.0003
## 160 1.1888 nan 0.0010 0.0003
## 180 1.1748 nan 0.0010 0.0003
## 200 1.1612 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1351 nan 0.0010 0.0003
## 260 1.1229 nan 0.0010 0.0002
## 280 1.1110 nan 0.0010 0.0003
## 300 1.0994 nan 0.0010 0.0002
## 320 1.0883 nan 0.0010 0.0002
## 340 1.0775 nan 0.0010 0.0002
## 360 1.0670 nan 0.0010 0.0002
## 380 1.0566 nan 0.0010 0.0002
## 400 1.0463 nan 0.0010 0.0002
## 420 1.0367 nan 0.0010 0.0002
## 440 1.0274 nan 0.0010 0.0002
## 460 1.0179 nan 0.0010 0.0002
## 480 1.0088 nan 0.0010 0.0002
## 500 1.0000 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0003
## 80 1.2521 nan 0.0010 0.0004
## 100 1.2363 nan 0.0010 0.0003
## 120 1.2208 nan 0.0010 0.0004
## 140 1.2060 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1779 nan 0.0010 0.0003
## 200 1.1646 nan 0.0010 0.0003
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1390 nan 0.0010 0.0003
## 260 1.1270 nan 0.0010 0.0003
## 280 1.1152 nan 0.0010 0.0003
## 300 1.1038 nan 0.0010 0.0002
## 320 1.0925 nan 0.0010 0.0002
## 340 1.0816 nan 0.0010 0.0003
## 360 1.0711 nan 0.0010 0.0002
## 380 1.0607 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
## 420 1.0409 nan 0.0010 0.0002
## 440 1.0312 nan 0.0010 0.0002
## 460 1.0218 nan 0.0010 0.0002
## 480 1.0126 nan 0.0010 0.0002
## 500 1.0038 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3045 nan 0.0100 0.0034
## 3 1.2965 nan 0.0100 0.0038
## 4 1.2885 nan 0.0100 0.0039
## 5 1.2806 nan 0.0100 0.0038
## 6 1.2725 nan 0.0100 0.0037
## 7 1.2649 nan 0.0100 0.0038
## 8 1.2574 nan 0.0100 0.0034
## 9 1.2498 nan 0.0100 0.0036
## 10 1.2426 nan 0.0100 0.0033
## 20 1.1760 nan 0.0100 0.0027
## 40 1.0706 nan 0.0100 0.0019
## 60 0.9896 nan 0.0100 0.0012
## 80 0.9270 nan 0.0100 0.0011
## 100 0.8769 nan 0.0100 0.0007
## 120 0.8356 nan 0.0100 0.0008
## 140 0.8029 nan 0.0100 0.0005
## 160 0.7756 nan 0.0100 0.0003
## 180 0.7504 nan 0.0100 0.0004
## 200 0.7295 nan 0.0100 0.0003
## 220 0.7101 nan 0.0100 0.0002
## 240 0.6923 nan 0.0100 0.0001
## 260 0.6769 nan 0.0100 0.0001
## 280 0.6620 nan 0.0100 0.0000
## 300 0.6484 nan 0.0100 0.0001
## 320 0.6356 nan 0.0100 -0.0001
## 340 0.6242 nan 0.0100 0.0001
## 360 0.6137 nan 0.0100 -0.0000
## 380 0.6022 nan 0.0100 0.0001
## 400 0.5920 nan 0.0100 0.0000
## 420 0.5818 nan 0.0100 0.0000
## 440 0.5727 nan 0.0100 -0.0000
## 460 0.5629 nan 0.0100 -0.0000
## 480 0.5541 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0037
## 3 1.2957 nan 0.0100 0.0041
## 4 1.2875 nan 0.0100 0.0040
## 5 1.2789 nan 0.0100 0.0036
## 6 1.2718 nan 0.0100 0.0036
## 7 1.2637 nan 0.0100 0.0035
## 8 1.2558 nan 0.0100 0.0037
## 9 1.2487 nan 0.0100 0.0032
## 10 1.2409 nan 0.0100 0.0032
## 20 1.1747 nan 0.0100 0.0028
## 40 1.0698 nan 0.0100 0.0017
## 60 0.9923 nan 0.0100 0.0012
## 80 0.9293 nan 0.0100 0.0011
## 100 0.8789 nan 0.0100 0.0008
## 120 0.8392 nan 0.0100 0.0006
## 140 0.8054 nan 0.0100 0.0005
## 160 0.7779 nan 0.0100 0.0005
## 180 0.7537 nan 0.0100 0.0002
## 200 0.7324 nan 0.0100 0.0002
## 220 0.7128 nan 0.0100 0.0003
## 240 0.6950 nan 0.0100 0.0002
## 260 0.6794 nan 0.0100 0.0001
## 280 0.6645 nan 0.0100 -0.0000
## 300 0.6512 nan 0.0100 -0.0001
## 320 0.6392 nan 0.0100 -0.0000
## 340 0.6281 nan 0.0100 -0.0001
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## 380 0.6080 nan 0.0100 0.0001
## 400 0.5985 nan 0.0100 -0.0000
## 420 0.5885 nan 0.0100 0.0000
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## 460 0.5710 nan 0.0100 -0.0000
## 480 0.5625 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0040
## 2 1.3053 nan 0.0100 0.0032
## 3 1.2967 nan 0.0100 0.0040
## 4 1.2885 nan 0.0100 0.0038
## 5 1.2810 nan 0.0100 0.0039
## 6 1.2746 nan 0.0100 0.0029
## 7 1.2663 nan 0.0100 0.0039
## 8 1.2591 nan 0.0100 0.0032
## 9 1.2519 nan 0.0100 0.0034
## 10 1.2451 nan 0.0100 0.0034
## 20 1.1774 nan 0.0100 0.0029
## 40 1.0736 nan 0.0100 0.0019
## 60 0.9958 nan 0.0100 0.0015
## 80 0.9343 nan 0.0100 0.0012
## 100 0.8835 nan 0.0100 0.0008
## 120 0.8420 nan 0.0100 0.0006
## 140 0.8087 nan 0.0100 0.0004
## 160 0.7804 nan 0.0100 0.0005
## 180 0.7556 nan 0.0100 0.0003
## 200 0.7350 nan 0.0100 0.0002
## 220 0.7164 nan 0.0100 -0.0001
## 240 0.6994 nan 0.0100 0.0001
## 260 0.6843 nan 0.0100 0.0001
## 280 0.6703 nan 0.0100 -0.0000
## 300 0.6577 nan 0.0100 0.0001
## 320 0.6454 nan 0.0100 -0.0000
## 340 0.6343 nan 0.0100 -0.0001
## 360 0.6238 nan 0.0100 -0.0000
## 380 0.6141 nan 0.0100 -0.0001
## 400 0.6047 nan 0.0100 -0.0001
## 420 0.5955 nan 0.0100 -0.0001
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## 460 0.5792 nan 0.0100 -0.0001
## 480 0.5704 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0043
## 2 1.3032 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2854 nan 0.0100 0.0037
## 5 1.2768 nan 0.0100 0.0039
## 6 1.2687 nan 0.0100 0.0036
## 7 1.2607 nan 0.0100 0.0036
## 8 1.2521 nan 0.0100 0.0036
## 9 1.2440 nan 0.0100 0.0038
## 10 1.2368 nan 0.0100 0.0032
## 20 1.1677 nan 0.0100 0.0029
## 40 1.0560 nan 0.0100 0.0021
## 60 0.9719 nan 0.0100 0.0015
## 80 0.9060 nan 0.0100 0.0013
## 100 0.8530 nan 0.0100 0.0008
## 120 0.8099 nan 0.0100 0.0005
## 140 0.7725 nan 0.0100 0.0006
## 160 0.7416 nan 0.0100 0.0005
## 180 0.7160 nan 0.0100 0.0004
## 200 0.6928 nan 0.0100 0.0001
## 220 0.6723 nan 0.0100 0.0002
## 240 0.6524 nan 0.0100 0.0002
## 260 0.6345 nan 0.0100 0.0002
## 280 0.6183 nan 0.0100 0.0002
## 300 0.6039 nan 0.0100 0.0000
## 320 0.5912 nan 0.0100 0.0001
## 340 0.5775 nan 0.0100 0.0001
## 360 0.5645 nan 0.0100 0.0001
## 380 0.5531 nan 0.0100 -0.0001
## 400 0.5416 nan 0.0100 0.0001
## 420 0.5316 nan 0.0100 0.0000
## 440 0.5210 nan 0.0100 -0.0001
## 460 0.5116 nan 0.0100 0.0000
## 480 0.5027 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0040
## 2 1.3032 nan 0.0100 0.0039
## 3 1.2943 nan 0.0100 0.0042
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0036
## 6 1.2695 nan 0.0100 0.0038
## 7 1.2618 nan 0.0100 0.0035
## 8 1.2546 nan 0.0100 0.0032
## 9 1.2470 nan 0.0100 0.0033
## 10 1.2389 nan 0.0100 0.0035
## 20 1.1698 nan 0.0100 0.0027
## 40 1.0581 nan 0.0100 0.0018
## 60 0.9751 nan 0.0100 0.0013
## 80 0.9095 nan 0.0100 0.0009
## 100 0.8577 nan 0.0100 0.0009
## 120 0.8135 nan 0.0100 0.0006
## 140 0.7787 nan 0.0100 0.0005
## 160 0.7485 nan 0.0100 0.0002
## 180 0.7225 nan 0.0100 0.0002
## 200 0.6997 nan 0.0100 0.0002
## 220 0.6798 nan 0.0100 0.0000
## 240 0.6623 nan 0.0100 0.0001
## 260 0.6447 nan 0.0100 0.0000
## 280 0.6292 nan 0.0100 0.0002
## 300 0.6146 nan 0.0100 -0.0001
## 320 0.6006 nan 0.0100 0.0001
## 340 0.5884 nan 0.0100 -0.0000
## 360 0.5753 nan 0.0100 -0.0001
## 380 0.5641 nan 0.0100 0.0000
## 400 0.5530 nan 0.0100 0.0000
## 420 0.5422 nan 0.0100 -0.0002
## 440 0.5317 nan 0.0100 -0.0001
## 460 0.5219 nan 0.0100 -0.0001
## 480 0.5120 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0041
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2849 nan 0.0100 0.0043
## 5 1.2771 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0032
## 7 1.2611 nan 0.0100 0.0037
## 8 1.2530 nan 0.0100 0.0034
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2380 nan 0.0100 0.0031
## 20 1.1684 nan 0.0100 0.0029
## 40 1.0594 nan 0.0100 0.0020
## 60 0.9764 nan 0.0100 0.0015
## 80 0.9130 nan 0.0100 0.0011
## 100 0.8613 nan 0.0100 0.0009
## 120 0.8184 nan 0.0100 0.0007
## 140 0.7828 nan 0.0100 0.0004
## 160 0.7532 nan 0.0100 0.0005
## 180 0.7259 nan 0.0100 0.0003
## 200 0.7030 nan 0.0100 0.0002
## 220 0.6827 nan 0.0100 -0.0000
## 240 0.6645 nan 0.0100 0.0003
## 260 0.6478 nan 0.0100 0.0002
## 280 0.6327 nan 0.0100 0.0002
## 300 0.6186 nan 0.0100 0.0002
## 320 0.6056 nan 0.0100 -0.0001
## 340 0.5928 nan 0.0100 0.0002
## 360 0.5804 nan 0.0100 -0.0000
## 380 0.5695 nan 0.0100 0.0001
## 400 0.5583 nan 0.0100 -0.0001
## 420 0.5481 nan 0.0100 -0.0001
## 440 0.5387 nan 0.0100 0.0001
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## 480 0.5199 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3023 nan 0.0100 0.0046
## 3 1.2929 nan 0.0100 0.0042
## 4 1.2837 nan 0.0100 0.0039
## 5 1.2750 nan 0.0100 0.0038
## 6 1.2670 nan 0.0100 0.0037
## 7 1.2587 nan 0.0100 0.0039
## 8 1.2504 nan 0.0100 0.0035
## 9 1.2423 nan 0.0100 0.0034
## 10 1.2341 nan 0.0100 0.0035
## 20 1.1606 nan 0.0100 0.0032
## 40 1.0448 nan 0.0100 0.0022
## 60 0.9571 nan 0.0100 0.0018
## 80 0.8883 nan 0.0100 0.0013
## 100 0.8307 nan 0.0100 0.0007
## 120 0.7847 nan 0.0100 0.0006
## 140 0.7456 nan 0.0100 0.0003
## 160 0.7142 nan 0.0100 0.0002
## 180 0.6857 nan 0.0100 0.0003
## 200 0.6599 nan 0.0100 0.0004
## 220 0.6358 nan 0.0100 0.0001
## 240 0.6156 nan 0.0100 -0.0001
## 260 0.5974 nan 0.0100 0.0001
## 280 0.5803 nan 0.0100 0.0002
## 300 0.5648 nan 0.0100 -0.0000
## 320 0.5506 nan 0.0100 0.0001
## 340 0.5356 nan 0.0100 0.0000
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## 380 0.5099 nan 0.0100 -0.0001
## 400 0.4980 nan 0.0100 0.0000
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## 460 0.4642 nan 0.0100 0.0001
## 480 0.4545 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3017 nan 0.0100 0.0043
## 3 1.2928 nan 0.0100 0.0040
## 4 1.2838 nan 0.0100 0.0038
## 5 1.2747 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0035
## 7 1.2583 nan 0.0100 0.0033
## 8 1.2496 nan 0.0100 0.0040
## 9 1.2423 nan 0.0100 0.0029
## 10 1.2346 nan 0.0100 0.0035
## 20 1.1619 nan 0.0100 0.0032
## 40 1.0448 nan 0.0100 0.0021
## 60 0.9579 nan 0.0100 0.0015
## 80 0.8892 nan 0.0100 0.0011
## 100 0.8353 nan 0.0100 0.0010
## 120 0.7923 nan 0.0100 0.0005
## 140 0.7546 nan 0.0100 0.0003
## 160 0.7214 nan 0.0100 0.0003
## 180 0.6943 nan 0.0100 0.0002
## 200 0.6715 nan 0.0100 -0.0000
## 220 0.6487 nan 0.0100 0.0003
## 240 0.6283 nan 0.0100 0.0001
## 260 0.6099 nan 0.0100 0.0000
## 280 0.5919 nan 0.0100 0.0000
## 300 0.5761 nan 0.0100 -0.0001
## 320 0.5623 nan 0.0100 0.0001
## 340 0.5492 nan 0.0100 -0.0002
## 360 0.5367 nan 0.0100 0.0000
## 380 0.5247 nan 0.0100 0.0001
## 400 0.5126 nan 0.0100 -0.0000
## 420 0.5011 nan 0.0100 -0.0000
## 440 0.4893 nan 0.0100 0.0000
## 460 0.4784 nan 0.0100 0.0000
## 480 0.4685 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0037
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2836 nan 0.0100 0.0040
## 5 1.2751 nan 0.0100 0.0040
## 6 1.2663 nan 0.0100 0.0037
## 7 1.2577 nan 0.0100 0.0038
## 8 1.2491 nan 0.0100 0.0034
## 9 1.2421 nan 0.0100 0.0035
## 10 1.2346 nan 0.0100 0.0032
## 20 1.1655 nan 0.0100 0.0031
## 40 1.0528 nan 0.0100 0.0017
## 60 0.9682 nan 0.0100 0.0015
## 80 0.9009 nan 0.0100 0.0010
## 100 0.8465 nan 0.0100 0.0009
## 120 0.8018 nan 0.0100 0.0008
## 140 0.7645 nan 0.0100 0.0007
## 160 0.7339 nan 0.0100 0.0002
## 180 0.7057 nan 0.0100 0.0003
## 200 0.6837 nan 0.0100 0.0001
## 220 0.6621 nan 0.0100 0.0002
## 240 0.6422 nan 0.0100 0.0002
## 260 0.6229 nan 0.0100 0.0001
## 280 0.6058 nan 0.0100 0.0002
## 300 0.5904 nan 0.0100 -0.0000
## 320 0.5759 nan 0.0100 0.0001
## 340 0.5625 nan 0.0100 0.0001
## 360 0.5486 nan 0.0100 0.0000
## 380 0.5373 nan 0.0100 -0.0001
## 400 0.5260 nan 0.0100 -0.0002
## 420 0.5149 nan 0.0100 -0.0000
## 440 0.5041 nan 0.0100 -0.0001
## 460 0.4937 nan 0.0100 0.0000
## 480 0.4835 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0358
## 2 1.1752 nan 0.1000 0.0304
## 3 1.1165 nan 0.1000 0.0247
## 4 1.0662 nan 0.1000 0.0218
## 5 1.0242 nan 0.1000 0.0166
## 6 0.9898 nan 0.1000 0.0150
## 7 0.9526 nan 0.1000 0.0168
## 8 0.9264 nan 0.1000 0.0088
## 9 0.9043 nan 0.1000 0.0061
## 10 0.8783 nan 0.1000 0.0084
## 20 0.7208 nan 0.1000 0.0031
## 40 0.6002 nan 0.1000 -0.0002
## 60 0.5127 nan 0.1000 -0.0022
## 80 0.4498 nan 0.1000 -0.0006
## 100 0.3956 nan 0.1000 -0.0011
## 120 0.3558 nan 0.1000 -0.0003
## 140 0.3185 nan 0.1000 -0.0009
## 160 0.2824 nan 0.1000 -0.0000
## 180 0.2536 nan 0.1000 -0.0004
## 200 0.2282 nan 0.1000 -0.0003
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## 240 0.1874 nan 0.1000 -0.0007
## 260 0.1691 nan 0.1000 0.0002
## 280 0.1532 nan 0.1000 -0.0001
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## 360 0.1070 nan 0.1000 0.0001
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## 480 0.0644 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0403
## 2 1.1692 nan 0.1000 0.0316
## 3 1.1088 nan 0.1000 0.0276
## 4 1.0610 nan 0.1000 0.0206
## 5 1.0175 nan 0.1000 0.0174
## 6 0.9834 nan 0.1000 0.0133
## 7 0.9494 nan 0.1000 0.0127
## 8 0.9213 nan 0.1000 0.0118
## 9 0.8969 nan 0.1000 0.0088
## 10 0.8750 nan 0.1000 0.0070
## 20 0.7253 nan 0.1000 0.0017
## 40 0.6038 nan 0.1000 -0.0022
## 60 0.5170 nan 0.1000 0.0008
## 80 0.4486 nan 0.1000 -0.0001
## 100 0.3993 nan 0.1000 -0.0011
## 120 0.3527 nan 0.1000 0.0001
## 140 0.3155 nan 0.1000 -0.0005
## 160 0.2876 nan 0.1000 -0.0007
## 180 0.2599 nan 0.1000 -0.0006
## 200 0.2331 nan 0.1000 0.0003
## 220 0.2102 nan 0.1000 -0.0008
## 240 0.1895 nan 0.1000 -0.0010
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## 280 0.1560 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0369
## 2 1.1697 nan 0.1000 0.0290
## 3 1.1128 nan 0.1000 0.0241
## 4 1.0638 nan 0.1000 0.0226
## 5 1.0218 nan 0.1000 0.0175
## 6 0.9861 nan 0.1000 0.0143
## 7 0.9521 nan 0.1000 0.0124
## 8 0.9258 nan 0.1000 0.0095
## 9 0.8981 nan 0.1000 0.0091
## 10 0.8749 nan 0.1000 0.0090
## 20 0.7392 nan 0.1000 0.0024
## 40 0.6119 nan 0.1000 -0.0007
## 60 0.5296 nan 0.1000 -0.0017
## 80 0.4699 nan 0.1000 -0.0001
## 100 0.4139 nan 0.1000 -0.0004
## 120 0.3705 nan 0.1000 -0.0023
## 140 0.3327 nan 0.1000 -0.0013
## 160 0.3025 nan 0.1000 -0.0002
## 180 0.2704 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2296 nan 0.1000 0.0412
## 2 1.1665 nan 0.1000 0.0269
## 3 1.1054 nan 0.1000 0.0272
## 4 1.0542 nan 0.1000 0.0220
## 5 1.0131 nan 0.1000 0.0180
## 6 0.9733 nan 0.1000 0.0165
## 7 0.9392 nan 0.1000 0.0157
## 8 0.9077 nan 0.1000 0.0120
## 9 0.8811 nan 0.1000 0.0089
## 10 0.8576 nan 0.1000 0.0058
## 20 0.7013 nan 0.1000 0.0027
## 40 0.5504 nan 0.1000 0.0009
## 60 0.4662 nan 0.1000 -0.0018
## 80 0.3911 nan 0.1000 0.0001
## 100 0.3401 nan 0.1000 -0.0016
## 120 0.2943 nan 0.1000 -0.0000
## 140 0.2581 nan 0.1000 -0.0003
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## 180 0.1982 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0400
## 2 1.1695 nan 0.1000 0.0277
## 3 1.1041 nan 0.1000 0.0260
## 4 1.0580 nan 0.1000 0.0202
## 5 1.0139 nan 0.1000 0.0201
## 6 0.9768 nan 0.1000 0.0160
## 7 0.9415 nan 0.1000 0.0137
## 8 0.9154 nan 0.1000 0.0083
## 9 0.8874 nan 0.1000 0.0112
## 10 0.8623 nan 0.1000 0.0102
## 20 0.6988 nan 0.1000 0.0023
## 40 0.5501 nan 0.1000 0.0008
## 60 0.4619 nan 0.1000 -0.0010
## 80 0.3922 nan 0.1000 0.0003
## 100 0.3391 nan 0.1000 -0.0003
## 120 0.2990 nan 0.1000 -0.0001
## 140 0.2584 nan 0.1000 -0.0008
## 160 0.2282 nan 0.1000 -0.0006
## 180 0.2062 nan 0.1000 -0.0005
## 200 0.1815 nan 0.1000 -0.0004
## 220 0.1602 nan 0.1000 -0.0002
## 240 0.1411 nan 0.1000 -0.0009
## 260 0.1256 nan 0.1000 -0.0004
## 280 0.1133 nan 0.1000 -0.0005
## 300 0.1013 nan 0.1000 -0.0004
## 320 0.0901 nan 0.1000 -0.0005
## 340 0.0807 nan 0.1000 -0.0004
## 360 0.0715 nan 0.1000 -0.0001
## 380 0.0644 nan 0.1000 -0.0001
## 400 0.0585 nan 0.1000 -0.0003
## 420 0.0521 nan 0.1000 -0.0002
## 440 0.0476 nan 0.1000 -0.0003
## 460 0.0432 nan 0.1000 -0.0001
## 480 0.0389 nan 0.1000 -0.0002
## 500 0.0358 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0347
## 2 1.1683 nan 0.1000 0.0303
## 3 1.1046 nan 0.1000 0.0252
## 4 1.0551 nan 0.1000 0.0200
## 5 1.0058 nan 0.1000 0.0218
## 6 0.9703 nan 0.1000 0.0149
## 7 0.9344 nan 0.1000 0.0126
## 8 0.9039 nan 0.1000 0.0122
## 9 0.8747 nan 0.1000 0.0106
## 10 0.8524 nan 0.1000 0.0089
## 20 0.6977 nan 0.1000 0.0040
## 40 0.5655 nan 0.1000 -0.0010
## 60 0.4718 nan 0.1000 0.0001
## 80 0.4061 nan 0.1000 -0.0006
## 100 0.3485 nan 0.1000 -0.0006
## 120 0.3049 nan 0.1000 -0.0015
## 140 0.2671 nan 0.1000 -0.0009
## 160 0.2351 nan 0.1000 0.0001
## 180 0.2078 nan 0.1000 -0.0007
## 200 0.1830 nan 0.1000 -0.0005
## 220 0.1620 nan 0.1000 -0.0003
## 240 0.1428 nan 0.1000 -0.0005
## 260 0.1277 nan 0.1000 -0.0003
## 280 0.1141 nan 0.1000 -0.0002
## 300 0.1031 nan 0.1000 -0.0003
## 320 0.0923 nan 0.1000 -0.0003
## 340 0.0833 nan 0.1000 -0.0004
## 360 0.0752 nan 0.1000 -0.0003
## 380 0.0678 nan 0.1000 -0.0002
## 400 0.0618 nan 0.1000 -0.0002
## 420 0.0562 nan 0.1000 -0.0002
## 440 0.0506 nan 0.1000 -0.0002
## 460 0.0458 nan 0.1000 -0.0003
## 480 0.0416 nan 0.1000 -0.0002
## 500 0.0379 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0387
## 2 1.1539 nan 0.1000 0.0296
## 3 1.0902 nan 0.1000 0.0275
## 4 1.0385 nan 0.1000 0.0241
## 5 0.9909 nan 0.1000 0.0188
## 6 0.9499 nan 0.1000 0.0175
## 7 0.9138 nan 0.1000 0.0134
## 8 0.8834 nan 0.1000 0.0112
## 9 0.8534 nan 0.1000 0.0079
## 10 0.8249 nan 0.1000 0.0115
## 20 0.6683 nan 0.1000 -0.0009
## 40 0.5142 nan 0.1000 -0.0010
## 60 0.4132 nan 0.1000 0.0010
## 80 0.3404 nan 0.1000 -0.0004
## 100 0.2777 nan 0.1000 0.0003
## 120 0.2316 nan 0.1000 -0.0009
## 140 0.1921 nan 0.1000 -0.0001
## 160 0.1633 nan 0.1000 -0.0004
## 180 0.1407 nan 0.1000 -0.0002
## 200 0.1208 nan 0.1000 -0.0002
## 220 0.1051 nan 0.1000 -0.0002
## 240 0.0925 nan 0.1000 -0.0002
## 260 0.0811 nan 0.1000 -0.0002
## 280 0.0698 nan 0.1000 -0.0001
## 300 0.0614 nan 0.1000 -0.0000
## 320 0.0545 nan 0.1000 -0.0002
## 340 0.0479 nan 0.1000 0.0000
## 360 0.0425 nan 0.1000 -0.0001
## 380 0.0377 nan 0.1000 -0.0001
## 400 0.0331 nan 0.1000 -0.0001
## 420 0.0296 nan 0.1000 -0.0001
## 440 0.0263 nan 0.1000 -0.0000
## 460 0.0231 nan 0.1000 -0.0000
## 480 0.0206 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0397
## 2 1.1515 nan 0.1000 0.0355
## 3 1.0871 nan 0.1000 0.0321
## 4 1.0350 nan 0.1000 0.0234
## 5 0.9874 nan 0.1000 0.0200
## 6 0.9496 nan 0.1000 0.0161
## 7 0.9155 nan 0.1000 0.0123
## 8 0.8798 nan 0.1000 0.0133
## 9 0.8503 nan 0.1000 0.0102
## 10 0.8247 nan 0.1000 0.0076
## 20 0.6626 nan 0.1000 0.0014
## 40 0.5103 nan 0.1000 0.0002
## 60 0.4190 nan 0.1000 -0.0011
## 80 0.3520 nan 0.1000 -0.0001
## 100 0.2921 nan 0.1000 0.0003
## 120 0.2461 nan 0.1000 -0.0006
## 140 0.2080 nan 0.1000 -0.0011
## 160 0.1774 nan 0.1000 -0.0005
## 180 0.1512 nan 0.1000 -0.0001
## 200 0.1289 nan 0.1000 -0.0005
## 220 0.1112 nan 0.1000 -0.0004
## 240 0.0973 nan 0.1000 -0.0006
## 260 0.0846 nan 0.1000 -0.0001
## 280 0.0743 nan 0.1000 -0.0003
## 300 0.0649 nan 0.1000 -0.0004
## 320 0.0568 nan 0.1000 -0.0001
## 340 0.0498 nan 0.1000 0.0000
## 360 0.0442 nan 0.1000 -0.0000
## 380 0.0392 nan 0.1000 -0.0001
## 400 0.0348 nan 0.1000 -0.0001
## 420 0.0305 nan 0.1000 -0.0001
## 440 0.0269 nan 0.1000 -0.0001
## 460 0.0236 nan 0.1000 -0.0001
## 480 0.0208 nan 0.1000 -0.0000
## 500 0.0184 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2325 nan 0.1000 0.0381
## 2 1.1601 nan 0.1000 0.0322
## 3 1.0948 nan 0.1000 0.0288
## 4 1.0455 nan 0.1000 0.0211
## 5 1.0002 nan 0.1000 0.0195
## 6 0.9634 nan 0.1000 0.0144
## 7 0.9291 nan 0.1000 0.0121
## 8 0.8974 nan 0.1000 0.0118
## 9 0.8719 nan 0.1000 0.0094
## 10 0.8458 nan 0.1000 0.0108
## 20 0.6820 nan 0.1000 0.0032
## 40 0.5295 nan 0.1000 0.0008
## 60 0.4409 nan 0.1000 -0.0015
## 80 0.3730 nan 0.1000 -0.0010
## 100 0.3152 nan 0.1000 -0.0013
## 120 0.2679 nan 0.1000 0.0002
## 140 0.2293 nan 0.1000 -0.0008
## 160 0.1970 nan 0.1000 -0.0010
## 180 0.1722 nan 0.1000 -0.0003
## 200 0.1504 nan 0.1000 -0.0009
## 220 0.1310 nan 0.1000 -0.0007
## 240 0.1151 nan 0.1000 -0.0006
## 260 0.1011 nan 0.1000 -0.0004
## 280 0.0884 nan 0.1000 -0.0004
## 300 0.0775 nan 0.1000 -0.0005
## 320 0.0687 nan 0.1000 -0.0001
## 340 0.0617 nan 0.1000 -0.0002
## 360 0.0545 nan 0.1000 -0.0002
## 380 0.0474 nan 0.1000 -0.0002
## 400 0.0423 nan 0.1000 -0.0002
## 420 0.0374 nan 0.1000 -0.0002
## 440 0.0332 nan 0.1000 -0.0001
## 460 0.0294 nan 0.1000 -0.0002
## 480 0.0260 nan 0.1000 -0.0001
## 500 0.0232 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0005
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2534 nan 0.0010 0.0003
## 100 1.2383 nan 0.0010 0.0004
## 120 1.2233 nan 0.0010 0.0003
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1676 nan 0.0010 0.0003
## 220 1.1548 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0003
## 260 1.1305 nan 0.0010 0.0002
## 280 1.1187 nan 0.0010 0.0003
## 300 1.1072 nan 0.0010 0.0003
## 320 1.0964 nan 0.0010 0.0002
## 340 1.0856 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0544 nan 0.0010 0.0002
## 420 1.0446 nan 0.0010 0.0002
## 440 1.0351 nan 0.0010 0.0002
## 460 1.0260 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0083 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0003
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0004
## 120 1.2235 nan 0.0010 0.0003
## 140 1.2090 nan 0.0010 0.0003
## 160 1.1949 nan 0.0010 0.0003
## 180 1.1815 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1556 nan 0.0010 0.0002
## 240 1.1435 nan 0.0010 0.0003
## 260 1.1313 nan 0.0010 0.0003
## 280 1.1196 nan 0.0010 0.0003
## 300 1.1082 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0861 nan 0.0010 0.0002
## 360 1.0758 nan 0.0010 0.0002
## 380 1.0654 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
## 440 1.0364 nan 0.0010 0.0002
## 460 1.0273 nan 0.0010 0.0002
## 480 1.0183 nan 0.0010 0.0002
## 500 1.0095 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0003
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0004
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0004
## 160 1.1962 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0003
## 200 1.1695 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1444 nan 0.0010 0.0003
## 260 1.1324 nan 0.0010 0.0003
## 280 1.1208 nan 0.0010 0.0003
## 300 1.1095 nan 0.0010 0.0003
## 320 1.0987 nan 0.0010 0.0002
## 340 1.0879 nan 0.0010 0.0002
## 360 1.0777 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0575 nan 0.0010 0.0002
## 420 1.0476 nan 0.0010 0.0002
## 440 1.0384 nan 0.0010 0.0001
## 460 1.0292 nan 0.0010 0.0002
## 480 1.0205 nan 0.0010 0.0002
## 500 1.0119 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0005
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0003
## 100 1.2327 nan 0.0010 0.0004
## 120 1.2169 nan 0.0010 0.0004
## 140 1.2012 nan 0.0010 0.0004
## 160 1.1862 nan 0.0010 0.0004
## 180 1.1718 nan 0.0010 0.0003
## 200 1.1580 nan 0.0010 0.0003
## 220 1.1443 nan 0.0010 0.0003
## 240 1.1313 nan 0.0010 0.0002
## 260 1.1186 nan 0.0010 0.0003
## 280 1.1061 nan 0.0010 0.0003
## 300 1.0941 nan 0.0010 0.0002
## 320 1.0824 nan 0.0010 0.0003
## 340 1.0708 nan 0.0010 0.0003
## 360 1.0598 nan 0.0010 0.0002
## 380 1.0491 nan 0.0010 0.0002
## 400 1.0387 nan 0.0010 0.0002
## 420 1.0285 nan 0.0010 0.0002
## 440 1.0187 nan 0.0010 0.0002
## 460 1.0090 nan 0.0010 0.0002
## 480 0.9996 nan 0.0010 0.0002
## 500 0.9906 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2341 nan 0.0010 0.0004
## 120 1.2186 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1884 nan 0.0010 0.0004
## 180 1.1741 nan 0.0010 0.0003
## 200 1.1601 nan 0.0010 0.0003
## 220 1.1468 nan 0.0010 0.0003
## 240 1.1336 nan 0.0010 0.0003
## 260 1.1208 nan 0.0010 0.0003
## 280 1.1084 nan 0.0010 0.0003
## 300 1.0962 nan 0.0010 0.0003
## 320 1.0848 nan 0.0010 0.0003
## 340 1.0734 nan 0.0010 0.0002
## 360 1.0624 nan 0.0010 0.0002
## 380 1.0518 nan 0.0010 0.0002
## 400 1.0411 nan 0.0010 0.0002
## 420 1.0310 nan 0.0010 0.0002
## 440 1.0211 nan 0.0010 0.0002
## 460 1.0113 nan 0.0010 0.0002
## 480 1.0020 nan 0.0010 0.0002
## 500 0.9930 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2343 nan 0.0010 0.0004
## 120 1.2188 nan 0.0010 0.0003
## 140 1.2041 nan 0.0010 0.0003
## 160 1.1894 nan 0.0010 0.0003
## 180 1.1751 nan 0.0010 0.0003
## 200 1.1610 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1347 nan 0.0010 0.0003
## 260 1.1221 nan 0.0010 0.0003
## 280 1.1098 nan 0.0010 0.0003
## 300 1.0978 nan 0.0010 0.0003
## 320 1.0861 nan 0.0010 0.0003
## 340 1.0749 nan 0.0010 0.0002
## 360 1.0639 nan 0.0010 0.0002
## 380 1.0535 nan 0.0010 0.0002
## 400 1.0433 nan 0.0010 0.0002
## 420 1.0333 nan 0.0010 0.0002
## 440 1.0237 nan 0.0010 0.0002
## 460 1.0143 nan 0.0010 0.0002
## 480 1.0051 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3181 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0005
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3010 nan 0.0010 0.0004
## 40 1.2818 nan 0.0010 0.0004
## 60 1.2635 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0004
## 100 1.2290 nan 0.0010 0.0003
## 120 1.2127 nan 0.0010 0.0004
## 140 1.1967 nan 0.0010 0.0004
## 160 1.1811 nan 0.0010 0.0004
## 180 1.1660 nan 0.0010 0.0003
## 200 1.1511 nan 0.0010 0.0003
## 220 1.1367 nan 0.0010 0.0003
## 240 1.1231 nan 0.0010 0.0003
## 260 1.1098 nan 0.0010 0.0002
## 280 1.0967 nan 0.0010 0.0003
## 300 1.0841 nan 0.0010 0.0003
## 320 1.0721 nan 0.0010 0.0003
## 340 1.0603 nan 0.0010 0.0002
## 360 1.0488 nan 0.0010 0.0003
## 380 1.0375 nan 0.0010 0.0003
## 400 1.0267 nan 0.0010 0.0002
## 420 1.0158 nan 0.0010 0.0003
## 440 1.0056 nan 0.0010 0.0002
## 460 0.9955 nan 0.0010 0.0002
## 480 0.9856 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2471 nan 0.0010 0.0004
## 100 1.2303 nan 0.0010 0.0004
## 120 1.2137 nan 0.0010 0.0004
## 140 1.1979 nan 0.0010 0.0004
## 160 1.1823 nan 0.0010 0.0004
## 180 1.1673 nan 0.0010 0.0003
## 200 1.1528 nan 0.0010 0.0003
## 220 1.1387 nan 0.0010 0.0004
## 240 1.1250 nan 0.0010 0.0003
## 260 1.1120 nan 0.0010 0.0003
## 280 1.0993 nan 0.0010 0.0003
## 300 1.0869 nan 0.0010 0.0002
## 320 1.0751 nan 0.0010 0.0003
## 340 1.0631 nan 0.0010 0.0002
## 360 1.0517 nan 0.0010 0.0002
## 380 1.0406 nan 0.0010 0.0002
## 400 1.0299 nan 0.0010 0.0002
## 420 1.0194 nan 0.0010 0.0002
## 440 1.0092 nan 0.0010 0.0002
## 460 0.9992 nan 0.0010 0.0002
## 480 0.9896 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0005
## 40 1.2837 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2486 nan 0.0010 0.0004
## 100 1.2317 nan 0.0010 0.0004
## 120 1.2157 nan 0.0010 0.0004
## 140 1.2000 nan 0.0010 0.0003
## 160 1.1847 nan 0.0010 0.0004
## 180 1.1700 nan 0.0010 0.0003
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## 220 1.1418 nan 0.0010 0.0003
## 240 1.1285 nan 0.0010 0.0003
## 260 1.1152 nan 0.0010 0.0003
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## 320 1.0783 nan 0.0010 0.0003
## 340 1.0665 nan 0.0010 0.0002
## 360 1.0553 nan 0.0010 0.0003
## 380 1.0443 nan 0.0010 0.0002
## 400 1.0337 nan 0.0010 0.0002
## 420 1.0234 nan 0.0010 0.0002
## 440 1.0135 nan 0.0010 0.0002
## 460 1.0035 nan 0.0010 0.0002
## 480 0.9941 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3031 nan 0.0100 0.0039
## 3 1.2952 nan 0.0100 0.0037
## 4 1.2866 nan 0.0100 0.0038
## 5 1.2784 nan 0.0100 0.0040
## 6 1.2707 nan 0.0100 0.0036
## 7 1.2635 nan 0.0100 0.0034
## 8 1.2550 nan 0.0100 0.0037
## 9 1.2473 nan 0.0100 0.0035
## 10 1.2397 nan 0.0100 0.0039
## 20 1.1680 nan 0.0100 0.0030
## 40 1.0536 nan 0.0100 0.0024
## 60 0.9652 nan 0.0100 0.0015
## 80 0.8977 nan 0.0100 0.0014
## 100 0.8442 nan 0.0100 0.0009
## 120 0.8001 nan 0.0100 0.0010
## 140 0.7647 nan 0.0100 0.0005
## 160 0.7339 nan 0.0100 0.0006
## 180 0.7086 nan 0.0100 0.0003
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## 220 0.6659 nan 0.0100 0.0001
## 240 0.6497 nan 0.0100 0.0002
## 260 0.6337 nan 0.0100 0.0001
## 280 0.6189 nan 0.0100 -0.0001
## 300 0.6046 nan 0.0100 0.0000
## 320 0.5919 nan 0.0100 0.0000
## 340 0.5799 nan 0.0100 -0.0001
## 360 0.5694 nan 0.0100 -0.0000
## 380 0.5599 nan 0.0100 -0.0002
## 400 0.5497 nan 0.0100 0.0001
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## 440 0.5319 nan 0.0100 -0.0000
## 460 0.5241 nan 0.0100 0.0001
## 480 0.5155 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0041
## 2 1.3032 nan 0.0100 0.0040
## 3 1.2944 nan 0.0100 0.0040
## 4 1.2857 nan 0.0100 0.0039
## 5 1.2776 nan 0.0100 0.0038
## 6 1.2692 nan 0.0100 0.0036
## 7 1.2612 nan 0.0100 0.0038
## 8 1.2531 nan 0.0100 0.0041
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2374 nan 0.0100 0.0033
## 20 1.1666 nan 0.0100 0.0028
## 40 1.0544 nan 0.0100 0.0021
## 60 0.9686 nan 0.0100 0.0017
## 80 0.9005 nan 0.0100 0.0014
## 100 0.8476 nan 0.0100 0.0010
## 120 0.8027 nan 0.0100 0.0005
## 140 0.7668 nan 0.0100 0.0006
## 160 0.7361 nan 0.0100 0.0005
## 180 0.7109 nan 0.0100 0.0004
## 200 0.6885 nan 0.0100 0.0001
## 220 0.6699 nan 0.0100 0.0001
## 240 0.6525 nan 0.0100 0.0003
## 260 0.6363 nan 0.0100 0.0000
## 280 0.6222 nan 0.0100 0.0000
## 300 0.6088 nan 0.0100 0.0001
## 320 0.5973 nan 0.0100 -0.0000
## 340 0.5864 nan 0.0100 0.0001
## 360 0.5763 nan 0.0100 0.0000
## 380 0.5670 nan 0.0100 -0.0001
## 400 0.5573 nan 0.0100 -0.0000
## 420 0.5484 nan 0.0100 -0.0000
## 440 0.5397 nan 0.0100 -0.0002
## 460 0.5314 nan 0.0100 -0.0000
## 480 0.5229 nan 0.0100 0.0001
## 500 0.5146 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0035
## 2 1.3049 nan 0.0100 0.0036
## 3 1.2961 nan 0.0100 0.0041
## 4 1.2872 nan 0.0100 0.0038
## 5 1.2792 nan 0.0100 0.0037
## 6 1.2704 nan 0.0100 0.0041
## 7 1.2621 nan 0.0100 0.0036
## 8 1.2547 nan 0.0100 0.0034
## 9 1.2472 nan 0.0100 0.0037
## 10 1.2394 nan 0.0100 0.0037
## 20 1.1692 nan 0.0100 0.0026
## 40 1.0600 nan 0.0100 0.0019
## 60 0.9721 nan 0.0100 0.0016
## 80 0.9044 nan 0.0100 0.0010
## 100 0.8512 nan 0.0100 0.0011
## 120 0.8074 nan 0.0100 0.0007
## 140 0.7717 nan 0.0100 0.0003
## 160 0.7415 nan 0.0100 0.0003
## 180 0.7150 nan 0.0100 0.0004
## 200 0.6944 nan 0.0100 0.0002
## 220 0.6748 nan 0.0100 0.0001
## 240 0.6575 nan 0.0100 0.0002
## 260 0.6429 nan 0.0100 0.0000
## 280 0.6295 nan 0.0100 -0.0001
## 300 0.6165 nan 0.0100 -0.0001
## 320 0.6045 nan 0.0100 0.0000
## 340 0.5933 nan 0.0100 0.0001
## 360 0.5833 nan 0.0100 -0.0000
## 380 0.5744 nan 0.0100 0.0001
## 400 0.5641 nan 0.0100 -0.0001
## 420 0.5555 nan 0.0100 -0.0001
## 440 0.5472 nan 0.0100 -0.0001
## 460 0.5395 nan 0.0100 -0.0001
## 480 0.5310 nan 0.0100 -0.0002
## 500 0.5231 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0045
## 2 1.3024 nan 0.0100 0.0047
## 3 1.2935 nan 0.0100 0.0039
## 4 1.2840 nan 0.0100 0.0041
## 5 1.2755 nan 0.0100 0.0042
## 6 1.2665 nan 0.0100 0.0037
## 7 1.2584 nan 0.0100 0.0033
## 8 1.2498 nan 0.0100 0.0035
## 9 1.2414 nan 0.0100 0.0034
## 10 1.2333 nan 0.0100 0.0036
## 20 1.1574 nan 0.0100 0.0034
## 40 1.0370 nan 0.0100 0.0024
## 60 0.9470 nan 0.0100 0.0016
## 80 0.8781 nan 0.0100 0.0011
## 100 0.8198 nan 0.0100 0.0011
## 120 0.7755 nan 0.0100 0.0008
## 140 0.7382 nan 0.0100 0.0004
## 160 0.7072 nan 0.0100 0.0004
## 180 0.6796 nan 0.0100 0.0005
## 200 0.6553 nan 0.0100 0.0003
## 220 0.6339 nan 0.0100 0.0001
## 240 0.6155 nan 0.0100 0.0003
## 260 0.5994 nan 0.0100 -0.0002
## 280 0.5827 nan 0.0100 0.0001
## 300 0.5687 nan 0.0100 0.0000
## 320 0.5557 nan 0.0100 -0.0001
## 340 0.5424 nan 0.0100 -0.0000
## 360 0.5304 nan 0.0100 0.0000
## 380 0.5185 nan 0.0100 0.0000
## 400 0.5077 nan 0.0100 0.0000
## 420 0.4970 nan 0.0100 -0.0000
## 440 0.4871 nan 0.0100 0.0000
## 460 0.4776 nan 0.0100 0.0000
## 480 0.4688 nan 0.0100 -0.0000
## 500 0.4599 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0046
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2923 nan 0.0100 0.0040
## 4 1.2834 nan 0.0100 0.0045
## 5 1.2746 nan 0.0100 0.0038
## 6 1.2653 nan 0.0100 0.0042
## 7 1.2572 nan 0.0100 0.0038
## 8 1.2493 nan 0.0100 0.0037
## 9 1.2412 nan 0.0100 0.0038
## 10 1.2332 nan 0.0100 0.0035
## 20 1.1595 nan 0.0100 0.0028
## 40 1.0404 nan 0.0100 0.0025
## 60 0.9512 nan 0.0100 0.0019
## 80 0.8797 nan 0.0100 0.0013
## 100 0.8232 nan 0.0100 0.0007
## 120 0.7777 nan 0.0100 0.0010
## 140 0.7395 nan 0.0100 0.0006
## 160 0.7076 nan 0.0100 0.0003
## 180 0.6809 nan 0.0100 0.0003
## 200 0.6563 nan 0.0100 0.0001
## 220 0.6353 nan 0.0100 0.0002
## 240 0.6164 nan 0.0100 0.0000
## 260 0.5996 nan 0.0100 0.0001
## 280 0.5849 nan 0.0100 0.0001
## 300 0.5715 nan 0.0100 0.0001
## 320 0.5586 nan 0.0100 -0.0001
## 340 0.5469 nan 0.0100 0.0001
## 360 0.5352 nan 0.0100 0.0000
## 380 0.5238 nan 0.0100 -0.0001
## 400 0.5135 nan 0.0100 -0.0000
## 420 0.5037 nan 0.0100 -0.0001
## 440 0.4943 nan 0.0100 -0.0002
## 460 0.4851 nan 0.0100 -0.0002
## 480 0.4761 nan 0.0100 -0.0001
## 500 0.4676 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3028 nan 0.0100 0.0034
## 3 1.2935 nan 0.0100 0.0045
## 4 1.2844 nan 0.0100 0.0041
## 5 1.2764 nan 0.0100 0.0037
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2590 nan 0.0100 0.0040
## 8 1.2509 nan 0.0100 0.0036
## 9 1.2431 nan 0.0100 0.0033
## 10 1.2351 nan 0.0100 0.0034
## 20 1.1635 nan 0.0100 0.0031
## 40 1.0441 nan 0.0100 0.0020
## 60 0.9556 nan 0.0100 0.0015
## 80 0.8853 nan 0.0100 0.0013
## 100 0.8282 nan 0.0100 0.0012
## 120 0.7840 nan 0.0100 0.0008
## 140 0.7469 nan 0.0100 0.0006
## 160 0.7151 nan 0.0100 0.0004
## 180 0.6870 nan 0.0100 0.0002
## 200 0.6641 nan 0.0100 0.0003
## 220 0.6439 nan 0.0100 -0.0002
## 240 0.6270 nan 0.0100 0.0002
## 260 0.6111 nan 0.0100 0.0000
## 280 0.5965 nan 0.0100 0.0000
## 300 0.5826 nan 0.0100 -0.0000
## 320 0.5701 nan 0.0100 0.0001
## 340 0.5588 nan 0.0100 -0.0001
## 360 0.5471 nan 0.0100 -0.0002
## 380 0.5358 nan 0.0100 0.0000
## 400 0.5255 nan 0.0100 0.0000
## 420 0.5156 nan 0.0100 -0.0000
## 440 0.5064 nan 0.0100 -0.0001
## 460 0.4962 nan 0.0100 -0.0000
## 480 0.4881 nan 0.0100 0.0000
## 500 0.4797 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0046
## 2 1.3013 nan 0.0100 0.0047
## 3 1.2914 nan 0.0100 0.0044
## 4 1.2818 nan 0.0100 0.0043
## 5 1.2735 nan 0.0100 0.0041
## 6 1.2653 nan 0.0100 0.0037
## 7 1.2566 nan 0.0100 0.0038
## 8 1.2472 nan 0.0100 0.0043
## 9 1.2393 nan 0.0100 0.0032
## 10 1.2310 nan 0.0100 0.0034
## 20 1.1546 nan 0.0100 0.0032
## 40 1.0318 nan 0.0100 0.0026
## 60 0.9385 nan 0.0100 0.0017
## 80 0.8650 nan 0.0100 0.0012
## 100 0.8049 nan 0.0100 0.0008
## 120 0.7556 nan 0.0100 0.0009
## 140 0.7144 nan 0.0100 0.0004
## 160 0.6811 nan 0.0100 0.0005
## 180 0.6536 nan 0.0100 0.0002
## 200 0.6293 nan 0.0100 0.0005
## 220 0.6062 nan 0.0100 0.0001
## 240 0.5860 nan 0.0100 0.0002
## 260 0.5665 nan 0.0100 0.0002
## 280 0.5491 nan 0.0100 0.0002
## 300 0.5337 nan 0.0100 -0.0002
## 320 0.5199 nan 0.0100 0.0001
## 340 0.5059 nan 0.0100 0.0000
## 360 0.4937 nan 0.0100 -0.0001
## 380 0.4800 nan 0.0100 0.0001
## 400 0.4679 nan 0.0100 0.0000
## 420 0.4574 nan 0.0100 -0.0001
## 440 0.4464 nan 0.0100 0.0000
## 460 0.4361 nan 0.0100 -0.0001
## 480 0.4264 nan 0.0100 -0.0001
## 500 0.4175 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0050
## 2 1.3010 nan 0.0100 0.0047
## 3 1.2907 nan 0.0100 0.0046
## 4 1.2815 nan 0.0100 0.0042
## 5 1.2725 nan 0.0100 0.0041
## 6 1.2632 nan 0.0100 0.0039
## 7 1.2540 nan 0.0100 0.0039
## 8 1.2451 nan 0.0100 0.0045
## 9 1.2367 nan 0.0100 0.0041
## 10 1.2286 nan 0.0100 0.0040
## 20 1.1528 nan 0.0100 0.0032
## 40 1.0306 nan 0.0100 0.0025
## 60 0.9357 nan 0.0100 0.0019
## 80 0.8622 nan 0.0100 0.0013
## 100 0.8019 nan 0.0100 0.0011
## 120 0.7550 nan 0.0100 0.0007
## 140 0.7167 nan 0.0100 0.0005
## 160 0.6832 nan 0.0100 0.0003
## 180 0.6557 nan 0.0100 0.0001
## 200 0.6309 nan 0.0100 0.0003
## 220 0.6083 nan 0.0100 0.0001
## 240 0.5886 nan 0.0100 0.0001
## 260 0.5708 nan 0.0100 0.0002
## 280 0.5545 nan 0.0100 0.0001
## 300 0.5401 nan 0.0100 0.0001
## 320 0.5260 nan 0.0100 0.0000
## 340 0.5118 nan 0.0100 -0.0001
## 360 0.4993 nan 0.0100 -0.0001
## 380 0.4880 nan 0.0100 -0.0000
## 400 0.4773 nan 0.0100 0.0000
## 420 0.4669 nan 0.0100 0.0001
## 440 0.4565 nan 0.0100 -0.0000
## 460 0.4462 nan 0.0100 -0.0001
## 480 0.4359 nan 0.0100 -0.0001
## 500 0.4264 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3020 nan 0.0100 0.0041
## 3 1.2927 nan 0.0100 0.0045
## 4 1.2834 nan 0.0100 0.0043
## 5 1.2748 nan 0.0100 0.0042
## 6 1.2652 nan 0.0100 0.0044
## 7 1.2564 nan 0.0100 0.0040
## 8 1.2463 nan 0.0100 0.0043
## 9 1.2377 nan 0.0100 0.0035
## 10 1.2293 nan 0.0100 0.0039
## 20 1.1521 nan 0.0100 0.0030
## 40 1.0325 nan 0.0100 0.0022
## 60 0.9403 nan 0.0100 0.0017
## 80 0.8680 nan 0.0100 0.0011
## 100 0.8095 nan 0.0100 0.0008
## 120 0.7616 nan 0.0100 0.0007
## 140 0.7225 nan 0.0100 0.0005
## 160 0.6905 nan 0.0100 0.0003
## 180 0.6619 nan 0.0100 0.0003
## 200 0.6384 nan 0.0100 0.0002
## 220 0.6172 nan 0.0100 0.0004
## 240 0.5964 nan 0.0100 0.0003
## 260 0.5802 nan 0.0100 0.0000
## 280 0.5644 nan 0.0100 -0.0001
## 300 0.5495 nan 0.0100 -0.0000
## 320 0.5350 nan 0.0100 -0.0001
## 340 0.5225 nan 0.0100 -0.0001
## 360 0.5103 nan 0.0100 -0.0002
## 380 0.4986 nan 0.0100 -0.0002
## 400 0.4871 nan 0.0100 0.0000
## 420 0.4776 nan 0.0100 -0.0000
## 440 0.4672 nan 0.0100 -0.0000
## 460 0.4580 nan 0.0100 -0.0001
## 480 0.4485 nan 0.0100 -0.0001
## 500 0.4387 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2409 nan 0.1000 0.0376
## 2 1.1674 nan 0.1000 0.0334
## 3 1.1042 nan 0.1000 0.0302
## 4 1.0469 nan 0.1000 0.0250
## 5 1.0013 nan 0.1000 0.0187
## 6 0.9596 nan 0.1000 0.0181
## 7 0.9234 nan 0.1000 0.0141
## 8 0.8953 nan 0.1000 0.0105
## 9 0.8705 nan 0.1000 0.0121
## 10 0.8460 nan 0.1000 0.0080
## 20 0.6871 nan 0.1000 0.0038
## 40 0.5550 nan 0.1000 -0.0007
## 60 0.4788 nan 0.1000 0.0002
## 80 0.4161 nan 0.1000 -0.0001
## 100 0.3672 nan 0.1000 -0.0007
## 120 0.3215 nan 0.1000 -0.0007
## 140 0.2874 nan 0.1000 -0.0010
## 160 0.2593 nan 0.1000 -0.0006
## 180 0.2331 nan 0.1000 -0.0003
## 200 0.2091 nan 0.1000 -0.0000
## 220 0.1900 nan 0.1000 -0.0005
## 240 0.1725 nan 0.1000 -0.0005
## 260 0.1583 nan 0.1000 -0.0003
## 280 0.1450 nan 0.1000 -0.0002
## 300 0.1327 nan 0.1000 -0.0004
## 320 0.1221 nan 0.1000 -0.0004
## 340 0.1125 nan 0.1000 -0.0006
## 360 0.1022 nan 0.1000 0.0000
## 380 0.0945 nan 0.1000 -0.0001
## 400 0.0878 nan 0.1000 -0.0002
## 420 0.0804 nan 0.1000 -0.0005
## 440 0.0743 nan 0.1000 -0.0001
## 460 0.0688 nan 0.1000 -0.0002
## 480 0.0636 nan 0.1000 -0.0000
## 500 0.0589 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0363
## 2 1.1628 nan 0.1000 0.0322
## 3 1.0994 nan 0.1000 0.0310
## 4 1.0475 nan 0.1000 0.0201
## 5 1.0022 nan 0.1000 0.0189
## 6 0.9618 nan 0.1000 0.0183
## 7 0.9246 nan 0.1000 0.0160
## 8 0.8950 nan 0.1000 0.0133
## 9 0.8704 nan 0.1000 0.0110
## 10 0.8399 nan 0.1000 0.0131
## 20 0.6891 nan 0.1000 0.0020
## 40 0.5571 nan 0.1000 -0.0003
## 60 0.4825 nan 0.1000 -0.0010
## 80 0.4206 nan 0.1000 -0.0007
## 100 0.3699 nan 0.1000 -0.0004
## 120 0.3311 nan 0.1000 0.0001
## 140 0.2961 nan 0.1000 -0.0012
## 160 0.2670 nan 0.1000 -0.0008
## 180 0.2400 nan 0.1000 -0.0010
## 200 0.2150 nan 0.1000 -0.0007
## 220 0.1956 nan 0.1000 -0.0006
## 240 0.1806 nan 0.1000 -0.0010
## 260 0.1633 nan 0.1000 -0.0009
## 280 0.1507 nan 0.1000 -0.0006
## 300 0.1371 nan 0.1000 -0.0004
## 320 0.1256 nan 0.1000 -0.0005
## 340 0.1162 nan 0.1000 -0.0002
## 360 0.1067 nan 0.1000 -0.0002
## 380 0.0981 nan 0.1000 -0.0003
## 400 0.0899 nan 0.1000 -0.0001
## 420 0.0822 nan 0.1000 -0.0003
## 440 0.0763 nan 0.1000 -0.0003
## 460 0.0695 nan 0.1000 -0.0001
## 480 0.0641 nan 0.1000 -0.0003
## 500 0.0595 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0335
## 2 1.1654 nan 0.1000 0.0316
## 3 1.0993 nan 0.1000 0.0298
## 4 1.0537 nan 0.1000 0.0194
## 5 1.0079 nan 0.1000 0.0194
## 6 0.9666 nan 0.1000 0.0160
## 7 0.9312 nan 0.1000 0.0144
## 8 0.8988 nan 0.1000 0.0142
## 9 0.8726 nan 0.1000 0.0105
## 10 0.8498 nan 0.1000 0.0062
## 20 0.7075 nan 0.1000 0.0018
## 40 0.5776 nan 0.1000 0.0005
## 60 0.4946 nan 0.1000 -0.0011
## 80 0.4385 nan 0.1000 -0.0003
## 100 0.3841 nan 0.1000 0.0000
## 120 0.3490 nan 0.1000 -0.0009
## 140 0.3140 nan 0.1000 -0.0011
## 160 0.2843 nan 0.1000 -0.0018
## 180 0.2563 nan 0.1000 -0.0006
## 200 0.2319 nan 0.1000 -0.0006
## 220 0.2137 nan 0.1000 -0.0010
## 240 0.1953 nan 0.1000 -0.0004
## 260 0.1803 nan 0.1000 -0.0008
## 280 0.1645 nan 0.1000 -0.0001
## 300 0.1513 nan 0.1000 -0.0006
## 320 0.1396 nan 0.1000 -0.0005
## 340 0.1287 nan 0.1000 -0.0009
## 360 0.1192 nan 0.1000 -0.0005
## 380 0.1101 nan 0.1000 -0.0006
## 400 0.1027 nan 0.1000 -0.0001
## 420 0.0944 nan 0.1000 -0.0004
## 440 0.0867 nan 0.1000 -0.0003
## 460 0.0808 nan 0.1000 -0.0003
## 480 0.0753 nan 0.1000 -0.0003
## 500 0.0703 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2298 nan 0.1000 0.0408
## 2 1.1522 nan 0.1000 0.0339
## 3 1.0882 nan 0.1000 0.0291
## 4 1.0342 nan 0.1000 0.0221
## 5 0.9854 nan 0.1000 0.0226
## 6 0.9468 nan 0.1000 0.0161
## 7 0.9061 nan 0.1000 0.0171
## 8 0.8728 nan 0.1000 0.0141
## 9 0.8452 nan 0.1000 0.0115
## 10 0.8197 nan 0.1000 0.0089
## 20 0.6470 nan 0.1000 0.0015
## 40 0.5027 nan 0.1000 0.0006
## 60 0.4175 nan 0.1000 -0.0006
## 80 0.3599 nan 0.1000 -0.0010
## 100 0.3105 nan 0.1000 0.0001
## 120 0.2732 nan 0.1000 -0.0002
## 140 0.2367 nan 0.1000 -0.0009
## 160 0.2086 nan 0.1000 0.0000
## 180 0.1832 nan 0.1000 -0.0010
## 200 0.1601 nan 0.1000 -0.0003
## 220 0.1425 nan 0.1000 -0.0004
## 240 0.1258 nan 0.1000 -0.0000
## 260 0.1142 nan 0.1000 -0.0003
## 280 0.1034 nan 0.1000 -0.0002
## 300 0.0934 nan 0.1000 -0.0006
## 320 0.0843 nan 0.1000 -0.0000
## 340 0.0766 nan 0.1000 -0.0003
## 360 0.0691 nan 0.1000 -0.0001
## 380 0.0612 nan 0.1000 -0.0001
## 400 0.0557 nan 0.1000 -0.0002
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## 460 0.0412 nan 0.1000 -0.0002
## 480 0.0374 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0424
## 2 1.1634 nan 0.1000 0.0297
## 3 1.0947 nan 0.1000 0.0303
## 4 1.0381 nan 0.1000 0.0260
## 5 0.9853 nan 0.1000 0.0207
## 6 0.9425 nan 0.1000 0.0175
## 7 0.9032 nan 0.1000 0.0147
## 8 0.8719 nan 0.1000 0.0120
## 9 0.8412 nan 0.1000 0.0125
## 10 0.8199 nan 0.1000 0.0073
## 20 0.6567 nan 0.1000 0.0041
## 40 0.5128 nan 0.1000 -0.0006
## 60 0.4234 nan 0.1000 0.0004
## 80 0.3618 nan 0.1000 -0.0011
## 100 0.3160 nan 0.1000 -0.0009
## 120 0.2769 nan 0.1000 0.0002
## 140 0.2437 nan 0.1000 -0.0012
## 160 0.2150 nan 0.1000 0.0003
## 180 0.1885 nan 0.1000 -0.0008
## 200 0.1672 nan 0.1000 -0.0003
## 220 0.1512 nan 0.1000 -0.0005
## 240 0.1366 nan 0.1000 -0.0004
## 260 0.1239 nan 0.1000 -0.0007
## 280 0.1126 nan 0.1000 -0.0005
## 300 0.0990 nan 0.1000 -0.0006
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## 360 0.0728 nan 0.1000 -0.0003
## 380 0.0661 nan 0.1000 -0.0003
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## 480 0.0396 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2255 nan 0.1000 0.0440
## 2 1.1555 nan 0.1000 0.0312
## 3 1.0908 nan 0.1000 0.0280
## 4 1.0356 nan 0.1000 0.0232
## 5 0.9901 nan 0.1000 0.0201
## 6 0.9504 nan 0.1000 0.0152
## 7 0.9130 nan 0.1000 0.0170
## 8 0.8796 nan 0.1000 0.0154
## 9 0.8502 nan 0.1000 0.0118
## 10 0.8266 nan 0.1000 0.0078
## 20 0.6706 nan 0.1000 0.0044
## 40 0.5387 nan 0.1000 -0.0010
## 60 0.4603 nan 0.1000 -0.0013
## 80 0.3815 nan 0.1000 -0.0001
## 100 0.3247 nan 0.1000 -0.0013
## 120 0.2812 nan 0.1000 -0.0001
## 140 0.2488 nan 0.1000 -0.0011
## 160 0.2181 nan 0.1000 -0.0001
## 180 0.1931 nan 0.1000 -0.0006
## 200 0.1731 nan 0.1000 -0.0010
## 220 0.1530 nan 0.1000 -0.0001
## 240 0.1374 nan 0.1000 -0.0008
## 260 0.1221 nan 0.1000 -0.0003
## 280 0.1092 nan 0.1000 -0.0002
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## 320 0.0887 nan 0.1000 -0.0000
## 340 0.0801 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0417
## 2 1.1512 nan 0.1000 0.0340
## 3 1.0809 nan 0.1000 0.0296
## 4 1.0205 nan 0.1000 0.0230
## 5 0.9744 nan 0.1000 0.0204
## 6 0.9341 nan 0.1000 0.0152
## 7 0.8929 nan 0.1000 0.0196
## 8 0.8646 nan 0.1000 0.0101
## 9 0.8326 nan 0.1000 0.0120
## 10 0.8052 nan 0.1000 0.0103
## 20 0.6331 nan 0.1000 0.0027
## 40 0.4766 nan 0.1000 -0.0007
## 60 0.3830 nan 0.1000 0.0011
## 80 0.3156 nan 0.1000 -0.0008
## 100 0.2633 nan 0.1000 -0.0002
## 120 0.2226 nan 0.1000 -0.0000
## 140 0.1930 nan 0.1000 -0.0004
## 160 0.1662 nan 0.1000 -0.0009
## 180 0.1441 nan 0.1000 -0.0000
## 200 0.1272 nan 0.1000 -0.0004
## 220 0.1101 nan 0.1000 -0.0004
## 240 0.0956 nan 0.1000 -0.0003
## 260 0.0844 nan 0.1000 -0.0002
## 280 0.0741 nan 0.1000 -0.0001
## 300 0.0654 nan 0.1000 -0.0002
## 320 0.0569 nan 0.1000 -0.0001
## 340 0.0502 nan 0.1000 -0.0001
## 360 0.0446 nan 0.1000 -0.0001
## 380 0.0393 nan 0.1000 -0.0001
## 400 0.0351 nan 0.1000 -0.0001
## 420 0.0319 nan 0.1000 -0.0001
## 440 0.0288 nan 0.1000 -0.0001
## 460 0.0252 nan 0.1000 -0.0001
## 480 0.0226 nan 0.1000 -0.0000
## 500 0.0200 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0378
## 2 1.1574 nan 0.1000 0.0339
## 3 1.0888 nan 0.1000 0.0318
## 4 1.0274 nan 0.1000 0.0282
## 5 0.9751 nan 0.1000 0.0238
## 6 0.9361 nan 0.1000 0.0150
## 7 0.8962 nan 0.1000 0.0158
## 8 0.8629 nan 0.1000 0.0136
## 9 0.8305 nan 0.1000 0.0114
## 10 0.8035 nan 0.1000 0.0081
## 20 0.6321 nan 0.1000 0.0015
## 40 0.4800 nan 0.1000 -0.0007
## 60 0.3893 nan 0.1000 -0.0005
## 80 0.3280 nan 0.1000 -0.0002
## 100 0.2727 nan 0.1000 -0.0007
## 120 0.2275 nan 0.1000 -0.0010
## 140 0.1918 nan 0.1000 -0.0005
## 160 0.1641 nan 0.1000 -0.0007
## 180 0.1404 nan 0.1000 -0.0007
## 200 0.1215 nan 0.1000 -0.0005
## 220 0.1061 nan 0.1000 -0.0008
## 240 0.0917 nan 0.1000 -0.0003
## 260 0.0801 nan 0.1000 -0.0003
## 280 0.0705 nan 0.1000 -0.0001
## 300 0.0616 nan 0.1000 -0.0003
## 320 0.0548 nan 0.1000 -0.0001
## 340 0.0476 nan 0.1000 -0.0001
## 360 0.0416 nan 0.1000 -0.0003
## 380 0.0369 nan 0.1000 -0.0002
## 400 0.0330 nan 0.1000 -0.0002
## 420 0.0291 nan 0.1000 -0.0000
## 440 0.0257 nan 0.1000 -0.0001
## 460 0.0226 nan 0.1000 -0.0000
## 480 0.0201 nan 0.1000 -0.0001
## 500 0.0178 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2331 nan 0.1000 0.0367
## 2 1.1535 nan 0.1000 0.0363
## 3 1.0821 nan 0.1000 0.0310
## 4 1.0267 nan 0.1000 0.0248
## 5 0.9776 nan 0.1000 0.0221
## 6 0.9358 nan 0.1000 0.0186
## 7 0.8964 nan 0.1000 0.0165
## 8 0.8613 nan 0.1000 0.0152
## 9 0.8321 nan 0.1000 0.0098
## 10 0.8041 nan 0.1000 0.0116
## 20 0.6353 nan 0.1000 0.0019
## 40 0.4888 nan 0.1000 -0.0007
## 60 0.3940 nan 0.1000 -0.0007
## 80 0.3318 nan 0.1000 -0.0006
## 100 0.2794 nan 0.1000 -0.0008
## 120 0.2377 nan 0.1000 -0.0010
## 140 0.2045 nan 0.1000 -0.0010
## 160 0.1763 nan 0.1000 -0.0012
## 180 0.1523 nan 0.1000 -0.0003
## 200 0.1315 nan 0.1000 -0.0008
## 220 0.1154 nan 0.1000 -0.0005
## 240 0.1024 nan 0.1000 -0.0002
## 260 0.0914 nan 0.1000 -0.0006
## 280 0.0814 nan 0.1000 -0.0003
## 300 0.0711 nan 0.1000 -0.0000
## 320 0.0632 nan 0.1000 -0.0002
## 340 0.0561 nan 0.1000 -0.0001
## 360 0.0503 nan 0.1000 -0.0002
## 380 0.0447 nan 0.1000 -0.0002
## 400 0.0397 nan 0.1000 -0.0001
## 420 0.0352 nan 0.1000 -0.0001
## 440 0.0314 nan 0.1000 -0.0001
## 460 0.0278 nan 0.1000 -0.0001
## 480 0.0249 nan 0.1000 -0.0002
## 500 0.0225 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2876 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2423 nan 0.0010 0.0002
## 120 1.2281 nan 0.0010 0.0003
## 140 1.2143 nan 0.0010 0.0003
## 160 1.2013 nan 0.0010 0.0003
## 180 1.1885 nan 0.0010 0.0003
## 200 1.1762 nan 0.0010 0.0003
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1518 nan 0.0010 0.0003
## 260 1.1402 nan 0.0010 0.0003
## 280 1.1289 nan 0.0010 0.0003
## 300 1.1180 nan 0.0010 0.0002
## 320 1.1072 nan 0.0010 0.0002
## 340 1.0970 nan 0.0010 0.0002
## 360 1.0870 nan 0.0010 0.0002
## 380 1.0773 nan 0.0010 0.0002
## 400 1.0677 nan 0.0010 0.0002
## 420 1.0587 nan 0.0010 0.0002
## 440 1.0495 nan 0.0010 0.0002
## 460 1.0409 nan 0.0010 0.0002
## 480 1.0324 nan 0.0010 0.0002
## 500 1.0240 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0003
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2567 nan 0.0010 0.0004
## 100 1.2421 nan 0.0010 0.0003
## 120 1.2282 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2010 nan 0.0010 0.0003
## 180 1.1880 nan 0.0010 0.0003
## 200 1.1756 nan 0.0010 0.0002
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1517 nan 0.0010 0.0003
## 260 1.1405 nan 0.0010 0.0003
## 280 1.1294 nan 0.0010 0.0003
## 300 1.1186 nan 0.0010 0.0002
## 320 1.1083 nan 0.0010 0.0002
## 340 1.0982 nan 0.0010 0.0002
## 360 1.0881 nan 0.0010 0.0002
## 380 1.0782 nan 0.0010 0.0002
## 400 1.0688 nan 0.0010 0.0002
## 420 1.0598 nan 0.0010 0.0002
## 440 1.0507 nan 0.0010 0.0002
## 460 1.0418 nan 0.0010 0.0002
## 480 1.0334 nan 0.0010 0.0002
## 500 1.0250 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2883 nan 0.0010 0.0003
## 60 1.2731 nan 0.0010 0.0003
## 80 1.2579 nan 0.0010 0.0004
## 100 1.2434 nan 0.0010 0.0003
## 120 1.2295 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2029 nan 0.0010 0.0003
## 180 1.1900 nan 0.0010 0.0003
## 200 1.1776 nan 0.0010 0.0003
## 220 1.1654 nan 0.0010 0.0003
## 240 1.1537 nan 0.0010 0.0003
## 260 1.1424 nan 0.0010 0.0002
## 280 1.1314 nan 0.0010 0.0002
## 300 1.1206 nan 0.0010 0.0003
## 320 1.1099 nan 0.0010 0.0002
## 340 1.0995 nan 0.0010 0.0002
## 360 1.0896 nan 0.0010 0.0002
## 380 1.0800 nan 0.0010 0.0002
## 400 1.0708 nan 0.0010 0.0002
## 420 1.0615 nan 0.0010 0.0002
## 440 1.0525 nan 0.0010 0.0002
## 460 1.0438 nan 0.0010 0.0002
## 480 1.0351 nan 0.0010 0.0002
## 500 1.0268 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2220 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1936 nan 0.0010 0.0003
## 180 1.1799 nan 0.0010 0.0003
## 200 1.1668 nan 0.0010 0.0003
## 220 1.1541 nan 0.0010 0.0002
## 240 1.1418 nan 0.0010 0.0003
## 260 1.1298 nan 0.0010 0.0003
## 280 1.1180 nan 0.0010 0.0002
## 300 1.1063 nan 0.0010 0.0002
## 320 1.0952 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0002
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0641 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
## 420 1.0443 nan 0.0010 0.0002
## 440 1.0347 nan 0.0010 0.0002
## 460 1.0254 nan 0.0010 0.0002
## 480 1.0165 nan 0.0010 0.0002
## 500 1.0077 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0003
## 100 1.2378 nan 0.0010 0.0004
## 120 1.2229 nan 0.0010 0.0003
## 140 1.2084 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1677 nan 0.0010 0.0003
## 220 1.1548 nan 0.0010 0.0003
## 240 1.1425 nan 0.0010 0.0003
## 260 1.1305 nan 0.0010 0.0002
## 280 1.1186 nan 0.0010 0.0003
## 300 1.1072 nan 0.0010 0.0003
## 320 1.0962 nan 0.0010 0.0003
## 340 1.0853 nan 0.0010 0.0002
## 360 1.0748 nan 0.0010 0.0002
## 380 1.0644 nan 0.0010 0.0002
## 400 1.0547 nan 0.0010 0.0002
## 420 1.0450 nan 0.0010 0.0002
## 440 1.0356 nan 0.0010 0.0002
## 460 1.0263 nan 0.0010 0.0002
## 480 1.0174 nan 0.0010 0.0002
## 500 1.0088 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0003
## 60 1.2700 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1963 nan 0.0010 0.0003
## 180 1.1830 nan 0.0010 0.0003
## 200 1.1698 nan 0.0010 0.0003
## 220 1.1572 nan 0.0010 0.0003
## 240 1.1448 nan 0.0010 0.0002
## 260 1.1327 nan 0.0010 0.0003
## 280 1.1208 nan 0.0010 0.0003
## 300 1.1093 nan 0.0010 0.0003
## 320 1.0983 nan 0.0010 0.0002
## 340 1.0876 nan 0.0010 0.0002
## 360 1.0771 nan 0.0010 0.0002
## 380 1.0670 nan 0.0010 0.0002
## 400 1.0570 nan 0.0010 0.0002
## 420 1.0477 nan 0.0010 0.0002
## 440 1.0384 nan 0.0010 0.0002
## 460 1.0292 nan 0.0010 0.0002
## 480 1.0203 nan 0.0010 0.0002
## 500 1.0116 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2670 nan 0.0010 0.0004
## 80 1.2502 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2026 nan 0.0010 0.0003
## 160 1.1879 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0003
## 200 1.1600 nan 0.0010 0.0003
## 220 1.1464 nan 0.0010 0.0003
## 240 1.1335 nan 0.0010 0.0003
## 260 1.1206 nan 0.0010 0.0002
## 280 1.1082 nan 0.0010 0.0002
## 300 1.0963 nan 0.0010 0.0002
## 320 1.0847 nan 0.0010 0.0003
## 340 1.0733 nan 0.0010 0.0003
## 360 1.0622 nan 0.0010 0.0002
## 380 1.0516 nan 0.0010 0.0002
## 400 1.0410 nan 0.0010 0.0002
## 420 1.0307 nan 0.0010 0.0003
## 440 1.0210 nan 0.0010 0.0002
## 460 1.0114 nan 0.0010 0.0002
## 480 1.0020 nan 0.0010 0.0002
## 500 0.9928 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2670 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2344 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2040 nan 0.0010 0.0003
## 160 1.1892 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1612 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0003
## 280 1.1103 nan 0.0010 0.0003
## 300 1.0984 nan 0.0010 0.0003
## 320 1.0867 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0646 nan 0.0010 0.0002
## 380 1.0542 nan 0.0010 0.0002
## 400 1.0438 nan 0.0010 0.0002
## 420 1.0337 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0144 nan 0.0010 0.0002
## 480 1.0049 nan 0.0010 0.0002
## 500 0.9958 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2849 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2517 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2202 nan 0.0010 0.0003
## 140 1.2054 nan 0.0010 0.0003
## 160 1.1913 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0003
## 200 1.1639 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0003
## 260 1.1259 nan 0.0010 0.0002
## 280 1.1136 nan 0.0010 0.0003
## 300 1.1019 nan 0.0010 0.0002
## 320 1.0905 nan 0.0010 0.0003
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0686 nan 0.0010 0.0002
## 380 1.0578 nan 0.0010 0.0002
## 400 1.0475 nan 0.0010 0.0002
## 420 1.0375 nan 0.0010 0.0002
## 440 1.0277 nan 0.0010 0.0002
## 460 1.0185 nan 0.0010 0.0002
## 480 1.0091 nan 0.0010 0.0002
## 500 1.0000 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0040
## 2 1.3020 nan 0.0100 0.0040
## 3 1.2941 nan 0.0100 0.0036
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2780 nan 0.0100 0.0034
## 6 1.2700 nan 0.0100 0.0036
## 7 1.2621 nan 0.0100 0.0034
## 8 1.2556 nan 0.0100 0.0031
## 9 1.2482 nan 0.0100 0.0032
## 10 1.2410 nan 0.0100 0.0034
## 20 1.1745 nan 0.0100 0.0026
## 40 1.0684 nan 0.0100 0.0023
## 60 0.9852 nan 0.0100 0.0017
## 80 0.9191 nan 0.0100 0.0011
## 100 0.8686 nan 0.0100 0.0007
## 120 0.8268 nan 0.0100 0.0007
## 140 0.7922 nan 0.0100 0.0005
## 160 0.7620 nan 0.0100 0.0003
## 180 0.7360 nan 0.0100 0.0004
## 200 0.7137 nan 0.0100 0.0003
## 220 0.6938 nan 0.0100 0.0001
## 240 0.6761 nan 0.0100 0.0001
## 260 0.6596 nan 0.0100 -0.0001
## 280 0.6450 nan 0.0100 0.0002
## 300 0.6327 nan 0.0100 0.0000
## 320 0.6204 nan 0.0100 -0.0001
## 340 0.6086 nan 0.0100 -0.0001
## 360 0.5980 nan 0.0100 -0.0000
## 380 0.5882 nan 0.0100 -0.0000
## 400 0.5781 nan 0.0100 -0.0001
## 420 0.5684 nan 0.0100 -0.0001
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## 460 0.5505 nan 0.0100 -0.0001
## 480 0.5414 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0037
## 2 1.3038 nan 0.0100 0.0036
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2872 nan 0.0100 0.0036
## 5 1.2795 nan 0.0100 0.0036
## 6 1.2722 nan 0.0100 0.0032
## 7 1.2650 nan 0.0100 0.0032
## 8 1.2578 nan 0.0100 0.0029
## 9 1.2499 nan 0.0100 0.0035
## 10 1.2421 nan 0.0100 0.0037
## 20 1.1743 nan 0.0100 0.0030
## 40 1.0657 nan 0.0100 0.0020
## 60 0.9846 nan 0.0100 0.0013
## 80 0.9222 nan 0.0100 0.0011
## 100 0.8685 nan 0.0100 0.0009
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## 220 0.6926 nan 0.0100 0.0002
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## 280 0.6470 nan 0.0100 0.0001
## 300 0.6340 nan 0.0100 0.0000
## 320 0.6217 nan 0.0100 -0.0001
## 340 0.6102 nan 0.0100 -0.0001
## 360 0.6001 nan 0.0100 -0.0000
## 380 0.5892 nan 0.0100 -0.0000
## 400 0.5798 nan 0.0100 0.0000
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## 460 0.5530 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0036
## 2 1.3048 nan 0.0100 0.0034
## 3 1.2963 nan 0.0100 0.0036
## 4 1.2881 nan 0.0100 0.0040
## 5 1.2807 nan 0.0100 0.0033
## 6 1.2724 nan 0.0100 0.0036
## 7 1.2643 nan 0.0100 0.0037
## 8 1.2574 nan 0.0100 0.0029
## 9 1.2496 nan 0.0100 0.0033
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## 20 1.1765 nan 0.0100 0.0027
## 40 1.0693 nan 0.0100 0.0018
## 60 0.9904 nan 0.0100 0.0015
## 80 0.9269 nan 0.0100 0.0012
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## 220 0.6992 nan 0.0100 0.0001
## 240 0.6816 nan 0.0100 0.0000
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## 320 0.6283 nan 0.0100 -0.0000
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## 380 0.5969 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3025 nan 0.0100 0.0041
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2852 nan 0.0100 0.0036
## 5 1.2773 nan 0.0100 0.0034
## 6 1.2692 nan 0.0100 0.0035
## 7 1.2614 nan 0.0100 0.0039
## 8 1.2532 nan 0.0100 0.0033
## 9 1.2453 nan 0.0100 0.0036
## 10 1.2373 nan 0.0100 0.0037
## 20 1.1686 nan 0.0100 0.0028
## 40 1.0558 nan 0.0100 0.0019
## 60 0.9685 nan 0.0100 0.0018
## 80 0.8992 nan 0.0100 0.0012
## 100 0.8450 nan 0.0100 0.0007
## 120 0.8001 nan 0.0100 0.0005
## 140 0.7625 nan 0.0100 0.0004
## 160 0.7314 nan 0.0100 0.0002
## 180 0.7053 nan 0.0100 0.0003
## 200 0.6801 nan 0.0100 0.0004
## 220 0.6590 nan 0.0100 0.0001
## 240 0.6404 nan 0.0100 -0.0000
## 260 0.6228 nan 0.0100 0.0000
## 280 0.6070 nan 0.0100 -0.0000
## 300 0.5921 nan 0.0100 -0.0000
## 320 0.5774 nan 0.0100 -0.0000
## 340 0.5650 nan 0.0100 -0.0001
## 360 0.5531 nan 0.0100 -0.0000
## 380 0.5421 nan 0.0100 -0.0001
## 400 0.5312 nan 0.0100 0.0000
## 420 0.5197 nan 0.0100 -0.0000
## 440 0.5097 nan 0.0100 -0.0000
## 460 0.4997 nan 0.0100 -0.0001
## 480 0.4902 nan 0.0100 -0.0000
## 500 0.4814 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0044
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0044
## 4 1.2853 nan 0.0100 0.0034
## 5 1.2769 nan 0.0100 0.0037
## 6 1.2689 nan 0.0100 0.0040
## 7 1.2601 nan 0.0100 0.0038
## 8 1.2520 nan 0.0100 0.0036
## 9 1.2437 nan 0.0100 0.0035
## 10 1.2363 nan 0.0100 0.0033
## 20 1.1655 nan 0.0100 0.0029
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9662 nan 0.0100 0.0015
## 80 0.8992 nan 0.0100 0.0011
## 100 0.8445 nan 0.0100 0.0011
## 120 0.8007 nan 0.0100 0.0005
## 140 0.7638 nan 0.0100 0.0005
## 160 0.7326 nan 0.0100 0.0005
## 180 0.7055 nan 0.0100 0.0003
## 200 0.6821 nan 0.0100 0.0003
## 220 0.6613 nan 0.0100 0.0001
## 240 0.6428 nan 0.0100 0.0003
## 260 0.6259 nan 0.0100 0.0002
## 280 0.6106 nan 0.0100 0.0002
## 300 0.5975 nan 0.0100 0.0002
## 320 0.5834 nan 0.0100 -0.0000
## 340 0.5711 nan 0.0100 0.0001
## 360 0.5588 nan 0.0100 -0.0001
## 380 0.5463 nan 0.0100 -0.0001
## 400 0.5366 nan 0.0100 -0.0001
## 420 0.5267 nan 0.0100 0.0000
## 440 0.5162 nan 0.0100 0.0000
## 460 0.5061 nan 0.0100 0.0001
## 480 0.4960 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0041
## 3 1.2945 nan 0.0100 0.0036
## 4 1.2864 nan 0.0100 0.0035
## 5 1.2780 nan 0.0100 0.0037
## 6 1.2693 nan 0.0100 0.0036
## 7 1.2617 nan 0.0100 0.0032
## 8 1.2546 nan 0.0100 0.0031
## 9 1.2476 nan 0.0100 0.0030
## 10 1.2403 nan 0.0100 0.0032
## 20 1.1691 nan 0.0100 0.0030
## 40 1.0578 nan 0.0100 0.0019
## 60 0.9729 nan 0.0100 0.0015
## 80 0.9041 nan 0.0100 0.0014
## 100 0.8495 nan 0.0100 0.0009
## 120 0.8059 nan 0.0100 0.0007
## 140 0.7689 nan 0.0100 0.0005
## 160 0.7353 nan 0.0100 0.0002
## 180 0.7103 nan 0.0100 0.0003
## 200 0.6877 nan 0.0100 0.0001
## 220 0.6674 nan 0.0100 0.0002
## 240 0.6491 nan 0.0100 0.0001
## 260 0.6318 nan 0.0100 0.0001
## 280 0.6156 nan 0.0100 0.0002
## 300 0.6011 nan 0.0100 0.0000
## 320 0.5892 nan 0.0100 0.0001
## 340 0.5776 nan 0.0100 -0.0001
## 360 0.5669 nan 0.0100 0.0001
## 380 0.5548 nan 0.0100 0.0001
## 400 0.5447 nan 0.0100 -0.0001
## 420 0.5343 nan 0.0100 0.0000
## 440 0.5248 nan 0.0100 0.0001
## 460 0.5165 nan 0.0100 -0.0001
## 480 0.5074 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0044
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2934 nan 0.0100 0.0041
## 4 1.2845 nan 0.0100 0.0040
## 5 1.2760 nan 0.0100 0.0038
## 6 1.2671 nan 0.0100 0.0041
## 7 1.2582 nan 0.0100 0.0038
## 8 1.2501 nan 0.0100 0.0035
## 9 1.2423 nan 0.0100 0.0038
## 10 1.2338 nan 0.0100 0.0037
## 20 1.1617 nan 0.0100 0.0024
## 40 1.0422 nan 0.0100 0.0021
## 60 0.9512 nan 0.0100 0.0017
## 80 0.8787 nan 0.0100 0.0010
## 100 0.8225 nan 0.0100 0.0009
## 120 0.7744 nan 0.0100 0.0007
## 140 0.7355 nan 0.0100 0.0003
## 160 0.7026 nan 0.0100 0.0004
## 180 0.6736 nan 0.0100 0.0003
## 200 0.6478 nan 0.0100 0.0003
## 220 0.6246 nan 0.0100 0.0002
## 240 0.6037 nan 0.0100 0.0003
## 260 0.5862 nan 0.0100 0.0001
## 280 0.5688 nan 0.0100 -0.0000
## 300 0.5518 nan 0.0100 0.0001
## 320 0.5374 nan 0.0100 0.0001
## 340 0.5236 nan 0.0100 0.0001
## 360 0.5107 nan 0.0100 -0.0000
## 380 0.4982 nan 0.0100 -0.0001
## 400 0.4867 nan 0.0100 -0.0000
## 420 0.4751 nan 0.0100 -0.0000
## 440 0.4647 nan 0.0100 0.0000
## 460 0.4540 nan 0.0100 0.0001
## 480 0.4445 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0041
## 3 1.2929 nan 0.0100 0.0040
## 4 1.2844 nan 0.0100 0.0040
## 5 1.2756 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0041
## 7 1.2585 nan 0.0100 0.0036
## 8 1.2500 nan 0.0100 0.0038
## 9 1.2423 nan 0.0100 0.0034
## 10 1.2340 nan 0.0100 0.0036
## 20 1.1611 nan 0.0100 0.0027
## 40 1.0418 nan 0.0100 0.0023
## 60 0.9507 nan 0.0100 0.0018
## 80 0.8803 nan 0.0100 0.0011
## 100 0.8247 nan 0.0100 0.0008
## 120 0.7783 nan 0.0100 0.0006
## 140 0.7382 nan 0.0100 0.0007
## 160 0.7058 nan 0.0100 0.0002
## 180 0.6775 nan 0.0100 0.0002
## 200 0.6512 nan 0.0100 0.0003
## 220 0.6285 nan 0.0100 0.0002
## 240 0.6078 nan 0.0100 0.0002
## 260 0.5898 nan 0.0100 0.0002
## 280 0.5738 nan 0.0100 -0.0001
## 300 0.5583 nan 0.0100 0.0001
## 320 0.5444 nan 0.0100 -0.0001
## 340 0.5325 nan 0.0100 -0.0001
## 360 0.5196 nan 0.0100 -0.0001
## 380 0.5078 nan 0.0100 -0.0001
## 400 0.4959 nan 0.0100 0.0000
## 420 0.4852 nan 0.0100 0.0000
## 440 0.4751 nan 0.0100 0.0000
## 460 0.4652 nan 0.0100 -0.0001
## 480 0.4555 nan 0.0100 0.0000
## 500 0.4456 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2926 nan 0.0100 0.0038
## 4 1.2837 nan 0.0100 0.0039
## 5 1.2749 nan 0.0100 0.0039
## 6 1.2663 nan 0.0100 0.0040
## 7 1.2578 nan 0.0100 0.0040
## 8 1.2498 nan 0.0100 0.0036
## 9 1.2415 nan 0.0100 0.0038
## 10 1.2335 nan 0.0100 0.0036
## 20 1.1595 nan 0.0100 0.0031
## 40 1.0460 nan 0.0100 0.0022
## 60 0.9569 nan 0.0100 0.0015
## 80 0.8875 nan 0.0100 0.0015
## 100 0.8322 nan 0.0100 0.0009
## 120 0.7857 nan 0.0100 0.0007
## 140 0.7480 nan 0.0100 0.0004
## 160 0.7154 nan 0.0100 0.0002
## 180 0.6872 nan 0.0100 0.0005
## 200 0.6618 nan 0.0100 0.0001
## 220 0.6403 nan 0.0100 0.0002
## 240 0.6211 nan 0.0100 0.0002
## 260 0.6017 nan 0.0100 0.0003
## 280 0.5849 nan 0.0100 0.0001
## 300 0.5695 nan 0.0100 0.0001
## 320 0.5553 nan 0.0100 -0.0001
## 340 0.5409 nan 0.0100 0.0001
## 360 0.5287 nan 0.0100 0.0001
## 380 0.5163 nan 0.0100 0.0000
## 400 0.5041 nan 0.0100 -0.0001
## 420 0.4928 nan 0.0100 -0.0001
## 440 0.4825 nan 0.0100 0.0000
## 460 0.4730 nan 0.0100 -0.0001
## 480 0.4628 nan 0.0100 -0.0002
## 500 0.4536 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0421
## 2 1.1638 nan 0.1000 0.0294
## 3 1.1084 nan 0.1000 0.0219
## 4 1.0613 nan 0.1000 0.0222
## 5 1.0177 nan 0.1000 0.0181
## 6 0.9789 nan 0.1000 0.0173
## 7 0.9433 nan 0.1000 0.0127
## 8 0.9130 nan 0.1000 0.0111
## 9 0.8858 nan 0.1000 0.0098
## 10 0.8612 nan 0.1000 0.0086
## 20 0.7126 nan 0.1000 0.0013
## 40 0.5758 nan 0.1000 -0.0015
## 60 0.4973 nan 0.1000 0.0000
## 80 0.4313 nan 0.1000 0.0001
## 100 0.3831 nan 0.1000 -0.0009
## 120 0.3423 nan 0.1000 0.0001
## 140 0.3043 nan 0.1000 -0.0004
## 160 0.2726 nan 0.1000 -0.0005
## 180 0.2498 nan 0.1000 -0.0004
## 200 0.2264 nan 0.1000 -0.0002
## 220 0.2031 nan 0.1000 -0.0003
## 240 0.1838 nan 0.1000 -0.0006
## 260 0.1680 nan 0.1000 -0.0001
## 280 0.1526 nan 0.1000 -0.0002
## 300 0.1396 nan 0.1000 -0.0004
## 320 0.1288 nan 0.1000 -0.0004
## 340 0.1189 nan 0.1000 -0.0002
## 360 0.1080 nan 0.1000 -0.0004
## 380 0.0983 nan 0.1000 -0.0006
## 400 0.0904 nan 0.1000 -0.0003
## 420 0.0830 nan 0.1000 -0.0005
## 440 0.0767 nan 0.1000 -0.0001
## 460 0.0703 nan 0.1000 -0.0003
## 480 0.0656 nan 0.1000 -0.0002
## 500 0.0614 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2380 nan 0.1000 0.0347
## 2 1.1687 nan 0.1000 0.0325
## 3 1.1158 nan 0.1000 0.0212
## 4 1.0686 nan 0.1000 0.0212
## 5 1.0154 nan 0.1000 0.0194
## 6 0.9678 nan 0.1000 0.0179
## 7 0.9325 nan 0.1000 0.0147
## 8 0.9008 nan 0.1000 0.0116
## 9 0.8752 nan 0.1000 0.0104
## 10 0.8519 nan 0.1000 0.0082
## 20 0.7013 nan 0.1000 0.0046
## 40 0.5657 nan 0.1000 -0.0008
## 60 0.4913 nan 0.1000 -0.0006
## 80 0.4356 nan 0.1000 0.0001
## 100 0.3878 nan 0.1000 -0.0019
## 120 0.3449 nan 0.1000 0.0001
## 140 0.3118 nan 0.1000 -0.0008
## 160 0.2800 nan 0.1000 -0.0017
## 180 0.2486 nan 0.1000 -0.0005
## 200 0.2271 nan 0.1000 -0.0006
## 220 0.2058 nan 0.1000 -0.0003
## 240 0.1857 nan 0.1000 -0.0004
## 260 0.1708 nan 0.1000 -0.0006
## 280 0.1561 nan 0.1000 -0.0007
## 300 0.1447 nan 0.1000 -0.0005
## 320 0.1332 nan 0.1000 -0.0001
## 340 0.1207 nan 0.1000 -0.0003
## 360 0.1119 nan 0.1000 -0.0002
## 380 0.1038 nan 0.1000 -0.0002
## 400 0.0964 nan 0.1000 -0.0001
## 420 0.0884 nan 0.1000 -0.0002
## 440 0.0826 nan 0.1000 -0.0002
## 460 0.0769 nan 0.1000 -0.0001
## 480 0.0710 nan 0.1000 -0.0002
## 500 0.0651 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0353
## 2 1.1711 nan 0.1000 0.0286
## 3 1.1163 nan 0.1000 0.0234
## 4 1.0634 nan 0.1000 0.0247
## 5 1.0209 nan 0.1000 0.0185
## 6 0.9785 nan 0.1000 0.0158
## 7 0.9494 nan 0.1000 0.0123
## 8 0.9170 nan 0.1000 0.0136
## 9 0.8907 nan 0.1000 0.0091
## 10 0.8653 nan 0.1000 0.0107
## 20 0.7169 nan 0.1000 0.0025
## 40 0.5883 nan 0.1000 -0.0003
## 60 0.5142 nan 0.1000 -0.0020
## 80 0.4600 nan 0.1000 -0.0013
## 100 0.4105 nan 0.1000 -0.0005
## 120 0.3670 nan 0.1000 0.0002
## 140 0.3302 nan 0.1000 0.0001
## 160 0.2980 nan 0.1000 -0.0005
## 180 0.2687 nan 0.1000 -0.0008
## 200 0.2452 nan 0.1000 -0.0009
## 220 0.2226 nan 0.1000 -0.0003
## 240 0.2044 nan 0.1000 -0.0010
## 260 0.1878 nan 0.1000 -0.0005
## 280 0.1728 nan 0.1000 -0.0007
## 300 0.1583 nan 0.1000 -0.0003
## 320 0.1454 nan 0.1000 -0.0006
## 340 0.1332 nan 0.1000 -0.0002
## 360 0.1232 nan 0.1000 -0.0003
## 380 0.1140 nan 0.1000 -0.0004
## 400 0.1054 nan 0.1000 -0.0002
## 420 0.0983 nan 0.1000 -0.0002
## 440 0.0912 nan 0.1000 -0.0003
## 460 0.0851 nan 0.1000 -0.0003
## 480 0.0784 nan 0.1000 -0.0001
## 500 0.0724 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2426 nan 0.1000 0.0358
## 2 1.1676 nan 0.1000 0.0341
## 3 1.1099 nan 0.1000 0.0262
## 4 1.0552 nan 0.1000 0.0247
## 5 1.0070 nan 0.1000 0.0219
## 6 0.9699 nan 0.1000 0.0137
## 7 0.9323 nan 0.1000 0.0150
## 8 0.8972 nan 0.1000 0.0159
## 9 0.8665 nan 0.1000 0.0106
## 10 0.8390 nan 0.1000 0.0116
## 20 0.6706 nan 0.1000 0.0014
## 40 0.5231 nan 0.1000 0.0002
## 60 0.4319 nan 0.1000 -0.0002
## 80 0.3595 nan 0.1000 -0.0004
## 100 0.3054 nan 0.1000 -0.0008
## 120 0.2664 nan 0.1000 -0.0006
## 140 0.2363 nan 0.1000 -0.0009
## 160 0.2061 nan 0.1000 -0.0006
## 180 0.1796 nan 0.1000 -0.0002
## 200 0.1614 nan 0.1000 -0.0004
## 220 0.1443 nan 0.1000 -0.0007
## 240 0.1289 nan 0.1000 -0.0000
## 260 0.1145 nan 0.1000 -0.0004
## 280 0.1019 nan 0.1000 -0.0002
## 300 0.0910 nan 0.1000 -0.0001
## 320 0.0824 nan 0.1000 -0.0003
## 340 0.0753 nan 0.1000 -0.0001
## 360 0.0676 nan 0.1000 -0.0003
## 380 0.0608 nan 0.1000 -0.0001
## 400 0.0557 nan 0.1000 -0.0002
## 420 0.0503 nan 0.1000 -0.0001
## 440 0.0451 nan 0.1000 -0.0001
## 460 0.0406 nan 0.1000 -0.0001
## 480 0.0366 nan 0.1000 -0.0000
## 500 0.0331 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2378 nan 0.1000 0.0400
## 2 1.1615 nan 0.1000 0.0321
## 3 1.0996 nan 0.1000 0.0264
## 4 1.0511 nan 0.1000 0.0221
## 5 1.0020 nan 0.1000 0.0210
## 6 0.9631 nan 0.1000 0.0154
## 7 0.9277 nan 0.1000 0.0171
## 8 0.8970 nan 0.1000 0.0135
## 9 0.8678 nan 0.1000 0.0101
## 10 0.8408 nan 0.1000 0.0099
## 20 0.6821 nan 0.1000 0.0039
## 40 0.5424 nan 0.1000 0.0003
## 60 0.4604 nan 0.1000 -0.0008
## 80 0.3896 nan 0.1000 -0.0008
## 100 0.3350 nan 0.1000 -0.0011
## 120 0.2931 nan 0.1000 -0.0012
## 140 0.2575 nan 0.1000 -0.0011
## 160 0.2269 nan 0.1000 -0.0015
## 180 0.1994 nan 0.1000 -0.0003
## 200 0.1777 nan 0.1000 -0.0005
## 220 0.1587 nan 0.1000 -0.0004
## 240 0.1420 nan 0.1000 -0.0010
## 260 0.1277 nan 0.1000 -0.0003
## 280 0.1141 nan 0.1000 -0.0003
## 300 0.1031 nan 0.1000 -0.0002
## 320 0.0935 nan 0.1000 -0.0001
## 340 0.0838 nan 0.1000 -0.0004
## 360 0.0769 nan 0.1000 -0.0001
## 380 0.0690 nan 0.1000 -0.0004
## 400 0.0619 nan 0.1000 -0.0001
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## 460 0.0453 nan 0.1000 -0.0002
## 480 0.0413 nan 0.1000 -0.0001
## 500 0.0376 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0374
## 2 1.1695 nan 0.1000 0.0302
## 3 1.1104 nan 0.1000 0.0260
## 4 1.0621 nan 0.1000 0.0221
## 5 1.0150 nan 0.1000 0.0206
## 6 0.9722 nan 0.1000 0.0184
## 7 0.9344 nan 0.1000 0.0156
## 8 0.9000 nan 0.1000 0.0161
## 9 0.8755 nan 0.1000 0.0087
## 10 0.8502 nan 0.1000 0.0100
## 20 0.6953 nan 0.1000 0.0009
## 40 0.5505 nan 0.1000 -0.0013
## 60 0.4680 nan 0.1000 -0.0021
## 80 0.4042 nan 0.1000 -0.0001
## 100 0.3445 nan 0.1000 -0.0004
## 120 0.3017 nan 0.1000 -0.0001
## 140 0.2641 nan 0.1000 -0.0008
## 160 0.2343 nan 0.1000 -0.0009
## 180 0.2064 nan 0.1000 -0.0010
## 200 0.1851 nan 0.1000 -0.0008
## 220 0.1663 nan 0.1000 -0.0012
## 240 0.1481 nan 0.1000 -0.0001
## 260 0.1325 nan 0.1000 -0.0000
## 280 0.1196 nan 0.1000 -0.0002
## 300 0.1084 nan 0.1000 -0.0004
## 320 0.0983 nan 0.1000 -0.0004
## 340 0.0890 nan 0.1000 -0.0001
## 360 0.0811 nan 0.1000 -0.0003
## 380 0.0735 nan 0.1000 -0.0002
## 400 0.0658 nan 0.1000 -0.0003
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## 440 0.0545 nan 0.1000 -0.0002
## 460 0.0496 nan 0.1000 -0.0002
## 480 0.0455 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0422
## 2 1.1549 nan 0.1000 0.0339
## 3 1.0931 nan 0.1000 0.0281
## 4 1.0332 nan 0.1000 0.0262
## 5 0.9899 nan 0.1000 0.0188
## 6 0.9524 nan 0.1000 0.0163
## 7 0.9108 nan 0.1000 0.0170
## 8 0.8768 nan 0.1000 0.0107
## 9 0.8490 nan 0.1000 0.0090
## 10 0.8207 nan 0.1000 0.0093
## 20 0.6518 nan 0.1000 0.0015
## 40 0.4941 nan 0.1000 -0.0005
## 60 0.3955 nan 0.1000 -0.0004
## 80 0.3235 nan 0.1000 -0.0007
## 100 0.2710 nan 0.1000 0.0003
## 120 0.2330 nan 0.1000 -0.0009
## 140 0.1964 nan 0.1000 -0.0006
## 160 0.1696 nan 0.1000 -0.0002
## 180 0.1466 nan 0.1000 -0.0007
## 200 0.1282 nan 0.1000 -0.0001
## 220 0.1113 nan 0.1000 -0.0003
## 240 0.0968 nan 0.1000 -0.0001
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## 280 0.0750 nan 0.1000 -0.0004
## 300 0.0654 nan 0.1000 -0.0002
## 320 0.0580 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0404
## 2 1.1547 nan 0.1000 0.0332
## 3 1.0949 nan 0.1000 0.0275
## 4 1.0395 nan 0.1000 0.0204
## 5 0.9935 nan 0.1000 0.0192
## 6 0.9530 nan 0.1000 0.0170
## 7 0.9154 nan 0.1000 0.0170
## 8 0.8823 nan 0.1000 0.0124
## 9 0.8497 nan 0.1000 0.0112
## 10 0.8229 nan 0.1000 0.0062
## 20 0.6575 nan 0.1000 0.0010
## 40 0.4956 nan 0.1000 -0.0004
## 60 0.4049 nan 0.1000 -0.0016
## 80 0.3380 nan 0.1000 -0.0012
## 100 0.2838 nan 0.1000 -0.0013
## 120 0.2401 nan 0.1000 -0.0010
## 140 0.2042 nan 0.1000 -0.0010
## 160 0.1759 nan 0.1000 -0.0008
## 180 0.1534 nan 0.1000 -0.0001
## 200 0.1334 nan 0.1000 -0.0006
## 220 0.1181 nan 0.1000 -0.0001
## 240 0.1042 nan 0.1000 -0.0004
## 260 0.0905 nan 0.1000 -0.0004
## 280 0.0796 nan 0.1000 -0.0001
## 300 0.0715 nan 0.1000 -0.0004
## 320 0.0630 nan 0.1000 -0.0001
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## 480 0.0239 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2316 nan 0.1000 0.0393
## 2 1.1574 nan 0.1000 0.0311
## 3 1.0926 nan 0.1000 0.0271
## 4 1.0354 nan 0.1000 0.0210
## 5 0.9900 nan 0.1000 0.0206
## 6 0.9455 nan 0.1000 0.0184
## 7 0.9067 nan 0.1000 0.0178
## 8 0.8762 nan 0.1000 0.0097
## 9 0.8497 nan 0.1000 0.0081
## 10 0.8199 nan 0.1000 0.0101
## 20 0.6527 nan 0.1000 0.0007
## 40 0.5134 nan 0.1000 -0.0032
## 60 0.4165 nan 0.1000 -0.0002
## 80 0.3411 nan 0.1000 -0.0007
## 100 0.2878 nan 0.1000 0.0001
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## 140 0.2138 nan 0.1000 -0.0004
## 160 0.1866 nan 0.1000 -0.0014
## 180 0.1647 nan 0.1000 -0.0012
## 200 0.1439 nan 0.1000 -0.0003
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## 460 0.0308 nan 0.1000 -0.0002
## 480 0.0280 nan 0.1000 -0.0002
## 500 0.0252 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1827 nan 0.0010 0.0003
## 200 1.1697 nan 0.0010 0.0003
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## 300 1.1104 nan 0.0010 0.0002
## 320 1.0997 nan 0.0010 0.0002
## 340 1.0892 nan 0.0010 0.0003
## 360 1.0785 nan 0.0010 0.0002
## 380 1.0686 nan 0.0010 0.0002
## 400 1.0590 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0002
## 440 1.0405 nan 0.0010 0.0002
## 460 1.0314 nan 0.0010 0.0002
## 480 1.0227 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2544 nan 0.0010 0.0003
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2104 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1832 nan 0.0010 0.0003
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## 380 1.0700 nan 0.0010 0.0002
## 400 1.0602 nan 0.0010 0.0002
## 420 1.0509 nan 0.0010 0.0002
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## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0003
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2116 nan 0.0010 0.0003
## 160 1.1982 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
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## 320 1.1032 nan 0.0010 0.0002
## 340 1.0928 nan 0.0010 0.0002
## 360 1.0827 nan 0.0010 0.0002
## 380 1.0729 nan 0.0010 0.0002
## 400 1.0634 nan 0.0010 0.0002
## 420 1.0541 nan 0.0010 0.0002
## 440 1.0450 nan 0.0010 0.0002
## 460 1.0361 nan 0.0010 0.0002
## 480 1.0273 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0003
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0004
## 100 1.2346 nan 0.0010 0.0003
## 120 1.2191 nan 0.0010 0.0004
## 140 1.2039 nan 0.0010 0.0004
## 160 1.1892 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
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## 280 1.1106 nan 0.0010 0.0002
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## 320 1.0872 nan 0.0010 0.0003
## 340 1.0763 nan 0.0010 0.0002
## 360 1.0655 nan 0.0010 0.0002
## 380 1.0550 nan 0.0010 0.0002
## 400 1.0446 nan 0.0010 0.0002
## 420 1.0346 nan 0.0010 0.0002
## 440 1.0251 nan 0.0010 0.0002
## 460 1.0156 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
## 500 0.9977 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2673 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0004
## 120 1.2195 nan 0.0010 0.0003
## 140 1.2046 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0004
## 180 1.1762 nan 0.0010 0.0003
## 200 1.1625 nan 0.0010 0.0003
## 220 1.1495 nan 0.0010 0.0003
## 240 1.1367 nan 0.0010 0.0002
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## 320 1.0891 nan 0.0010 0.0002
## 340 1.0780 nan 0.0010 0.0003
## 360 1.0674 nan 0.0010 0.0002
## 380 1.0569 nan 0.0010 0.0002
## 400 1.0466 nan 0.0010 0.0002
## 420 1.0369 nan 0.0010 0.0002
## 440 1.0272 nan 0.0010 0.0002
## 460 1.0179 nan 0.0010 0.0002
## 480 1.0089 nan 0.0010 0.0002
## 500 1.0001 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2859 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0003
## 120 1.2220 nan 0.0010 0.0004
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1933 nan 0.0010 0.0003
## 180 1.1796 nan 0.0010 0.0003
## 200 1.1660 nan 0.0010 0.0003
## 220 1.1532 nan 0.0010 0.0003
## 240 1.1403 nan 0.0010 0.0003
## 260 1.1281 nan 0.0010 0.0003
## 280 1.1162 nan 0.0010 0.0003
## 300 1.1048 nan 0.0010 0.0002
## 320 1.0936 nan 0.0010 0.0002
## 340 1.0826 nan 0.0010 0.0002
## 360 1.0720 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0512 nan 0.0010 0.0002
## 420 1.0413 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0225 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3012 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2640 nan 0.0010 0.0004
## 80 1.2466 nan 0.0010 0.0003
## 100 1.2299 nan 0.0010 0.0004
## 120 1.2135 nan 0.0010 0.0004
## 140 1.1975 nan 0.0010 0.0003
## 160 1.1825 nan 0.0010 0.0004
## 180 1.1680 nan 0.0010 0.0003
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## 220 1.1401 nan 0.0010 0.0003
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## 280 1.1013 nan 0.0010 0.0002
## 300 1.0890 nan 0.0010 0.0003
## 320 1.0774 nan 0.0010 0.0002
## 340 1.0658 nan 0.0010 0.0002
## 360 1.0545 nan 0.0010 0.0002
## 380 1.0435 nan 0.0010 0.0002
## 400 1.0330 nan 0.0010 0.0002
## 420 1.0227 nan 0.0010 0.0002
## 440 1.0126 nan 0.0010 0.0002
## 460 1.0028 nan 0.0010 0.0002
## 480 0.9932 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0005
## 20 1.3016 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2655 nan 0.0010 0.0004
## 80 1.2482 nan 0.0010 0.0004
## 100 1.2317 nan 0.0010 0.0004
## 120 1.2155 nan 0.0010 0.0004
## 140 1.2002 nan 0.0010 0.0003
## 160 1.1851 nan 0.0010 0.0003
## 180 1.1707 nan 0.0010 0.0003
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## 220 1.1428 nan 0.0010 0.0003
## 240 1.1294 nan 0.0010 0.0003
## 260 1.1165 nan 0.0010 0.0003
## 280 1.1042 nan 0.0010 0.0002
## 300 1.0918 nan 0.0010 0.0003
## 320 1.0801 nan 0.0010 0.0002
## 340 1.0687 nan 0.0010 0.0003
## 360 1.0573 nan 0.0010 0.0002
## 380 1.0466 nan 0.0010 0.0002
## 400 1.0362 nan 0.0010 0.0002
## 420 1.0260 nan 0.0010 0.0002
## 440 1.0160 nan 0.0010 0.0002
## 460 1.0063 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2335 nan 0.0010 0.0004
## 120 1.2177 nan 0.0010 0.0003
## 140 1.2026 nan 0.0010 0.0004
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1735 nan 0.0010 0.0003
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## 220 1.1460 nan 0.0010 0.0003
## 240 1.1330 nan 0.0010 0.0003
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## 280 1.1077 nan 0.0010 0.0003
## 300 1.0961 nan 0.0010 0.0002
## 320 1.0847 nan 0.0010 0.0002
## 340 1.0734 nan 0.0010 0.0002
## 360 1.0626 nan 0.0010 0.0002
## 380 1.0516 nan 0.0010 0.0003
## 400 1.0413 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0215 nan 0.0010 0.0002
## 460 1.0120 nan 0.0010 0.0002
## 480 1.0026 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0034
## 2 1.3038 nan 0.0100 0.0041
## 3 1.2946 nan 0.0100 0.0043
## 4 1.2858 nan 0.0100 0.0038
## 5 1.2778 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0041
## 7 1.2609 nan 0.0100 0.0039
## 8 1.2536 nan 0.0100 0.0032
## 9 1.2456 nan 0.0100 0.0038
## 10 1.2383 nan 0.0100 0.0033
## 20 1.1696 nan 0.0100 0.0023
## 40 1.0596 nan 0.0100 0.0020
## 60 0.9749 nan 0.0100 0.0015
## 80 0.9086 nan 0.0100 0.0011
## 100 0.8551 nan 0.0100 0.0011
## 120 0.8111 nan 0.0100 0.0005
## 140 0.7764 nan 0.0100 0.0005
## 160 0.7466 nan 0.0100 0.0003
## 180 0.7220 nan 0.0100 0.0002
## 200 0.7003 nan 0.0100 0.0002
## 220 0.6801 nan 0.0100 0.0001
## 240 0.6637 nan 0.0100 0.0001
## 260 0.6480 nan 0.0100 -0.0000
## 280 0.6340 nan 0.0100 0.0001
## 300 0.6208 nan 0.0100 0.0001
## 320 0.6095 nan 0.0100 0.0001
## 340 0.5993 nan 0.0100 -0.0000
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## 380 0.5786 nan 0.0100 -0.0002
## 400 0.5689 nan 0.0100 0.0000
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## 460 0.5416 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0040
## 3 1.2943 nan 0.0100 0.0038
## 4 1.2866 nan 0.0100 0.0035
## 5 1.2775 nan 0.0100 0.0041
## 6 1.2691 nan 0.0100 0.0037
## 7 1.2618 nan 0.0100 0.0033
## 8 1.2542 nan 0.0100 0.0035
## 9 1.2463 nan 0.0100 0.0035
## 10 1.2388 nan 0.0100 0.0036
## 20 1.1700 nan 0.0100 0.0032
## 40 1.0600 nan 0.0100 0.0018
## 60 0.9775 nan 0.0100 0.0017
## 80 0.9119 nan 0.0100 0.0009
## 100 0.8589 nan 0.0100 0.0008
## 120 0.8172 nan 0.0100 0.0007
## 140 0.7813 nan 0.0100 0.0005
## 160 0.7530 nan 0.0100 0.0003
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## 340 0.6061 nan 0.0100 0.0000
## 360 0.5956 nan 0.0100 0.0000
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## 400 0.5755 nan 0.0100 -0.0001
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## 460 0.5495 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2868 nan 0.0100 0.0034
## 5 1.2789 nan 0.0100 0.0034
## 6 1.2714 nan 0.0100 0.0036
## 7 1.2642 nan 0.0100 0.0034
## 8 1.2567 nan 0.0100 0.0029
## 9 1.2495 nan 0.0100 0.0034
## 10 1.2419 nan 0.0100 0.0031
## 20 1.1708 nan 0.0100 0.0030
## 40 1.0609 nan 0.0100 0.0020
## 60 0.9789 nan 0.0100 0.0015
## 80 0.9144 nan 0.0100 0.0012
## 100 0.8630 nan 0.0100 0.0007
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## 140 0.7876 nan 0.0100 0.0004
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## 320 0.6251 nan 0.0100 0.0000
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## 360 0.6042 nan 0.0100 -0.0001
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## 400 0.5841 nan 0.0100 0.0000
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## 460 0.5592 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0043
## 3 1.2930 nan 0.0100 0.0040
## 4 1.2844 nan 0.0100 0.0038
## 5 1.2755 nan 0.0100 0.0042
## 6 1.2667 nan 0.0100 0.0040
## 7 1.2581 nan 0.0100 0.0040
## 8 1.2497 nan 0.0100 0.0041
## 9 1.2412 nan 0.0100 0.0035
## 10 1.2337 nan 0.0100 0.0030
## 20 1.1590 nan 0.0100 0.0032
## 40 1.0439 nan 0.0100 0.0022
## 60 0.9549 nan 0.0100 0.0014
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## 100 0.8317 nan 0.0100 0.0009
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## 280 0.5947 nan 0.0100 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0038
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2839 nan 0.0100 0.0041
## 5 1.2746 nan 0.0100 0.0038
## 6 1.2660 nan 0.0100 0.0036
## 7 1.2570 nan 0.0100 0.0042
## 8 1.2488 nan 0.0100 0.0037
## 9 1.2407 nan 0.0100 0.0039
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## 20 1.1566 nan 0.0100 0.0030
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## 60 0.9564 nan 0.0100 0.0017
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## 320 0.5827 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2940 nan 0.0100 0.0039
## 4 1.2855 nan 0.0100 0.0041
## 5 1.2774 nan 0.0100 0.0034
## 6 1.2689 nan 0.0100 0.0039
## 7 1.2607 nan 0.0100 0.0037
## 8 1.2528 nan 0.0100 0.0038
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## 20 1.1661 nan 0.0100 0.0028
## 40 1.0493 nan 0.0100 0.0022
## 60 0.9617 nan 0.0100 0.0018
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## 440 0.5282 nan 0.0100 -0.0000
## 460 0.5181 nan 0.0100 -0.0000
## 480 0.5091 nan 0.0100 0.0001
## 500 0.5002 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3102 nan 0.0100 0.0041
## 2 1.3005 nan 0.0100 0.0042
## 3 1.2915 nan 0.0100 0.0040
## 4 1.2823 nan 0.0100 0.0042
## 5 1.2731 nan 0.0100 0.0041
## 6 1.2640 nan 0.0100 0.0041
## 7 1.2544 nan 0.0100 0.0041
## 8 1.2452 nan 0.0100 0.0042
## 9 1.2365 nan 0.0100 0.0040
## 10 1.2281 nan 0.0100 0.0038
## 20 1.1507 nan 0.0100 0.0030
## 40 1.0283 nan 0.0100 0.0020
## 60 0.9383 nan 0.0100 0.0016
## 80 0.8666 nan 0.0100 0.0010
## 100 0.8113 nan 0.0100 0.0007
## 120 0.7646 nan 0.0100 0.0007
## 140 0.7264 nan 0.0100 0.0005
## 160 0.6946 nan 0.0100 0.0004
## 180 0.6647 nan 0.0100 0.0003
## 200 0.6392 nan 0.0100 0.0004
## 220 0.6172 nan 0.0100 0.0002
## 240 0.5980 nan 0.0100 0.0000
## 260 0.5804 nan 0.0100 0.0001
## 280 0.5634 nan 0.0100 0.0001
## 300 0.5477 nan 0.0100 0.0000
## 320 0.5343 nan 0.0100 -0.0000
## 340 0.5206 nan 0.0100 0.0000
## 360 0.5077 nan 0.0100 -0.0000
## 380 0.4947 nan 0.0100 0.0002
## 400 0.4825 nan 0.0100 0.0002
## 420 0.4712 nan 0.0100 0.0002
## 440 0.4610 nan 0.0100 0.0000
## 460 0.4507 nan 0.0100 -0.0001
## 480 0.4414 nan 0.0100 -0.0001
## 500 0.4317 nan 0.0100 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0042
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2912 nan 0.0100 0.0047
## 4 1.2819 nan 0.0100 0.0043
## 5 1.2733 nan 0.0100 0.0041
## 6 1.2645 nan 0.0100 0.0042
## 7 1.2563 nan 0.0100 0.0036
## 8 1.2472 nan 0.0100 0.0039
## 9 1.2394 nan 0.0100 0.0032
## 10 1.2306 nan 0.0100 0.0038
## 20 1.1561 nan 0.0100 0.0032
## 40 1.0346 nan 0.0100 0.0022
## 60 0.9442 nan 0.0100 0.0016
## 80 0.8721 nan 0.0100 0.0013
## 100 0.8163 nan 0.0100 0.0009
## 120 0.7695 nan 0.0100 0.0007
## 140 0.7311 nan 0.0100 0.0005
## 160 0.6998 nan 0.0100 0.0005
## 180 0.6719 nan 0.0100 0.0003
## 200 0.6486 nan 0.0100 0.0002
## 220 0.6277 nan 0.0100 0.0002
## 240 0.6084 nan 0.0100 0.0003
## 260 0.5894 nan 0.0100 0.0001
## 280 0.5719 nan 0.0100 0.0003
## 300 0.5568 nan 0.0100 0.0001
## 320 0.5434 nan 0.0100 0.0000
## 340 0.5303 nan 0.0100 0.0001
## 360 0.5175 nan 0.0100 -0.0000
## 380 0.5054 nan 0.0100 -0.0001
## 400 0.4933 nan 0.0100 -0.0000
## 420 0.4822 nan 0.0100 0.0001
## 440 0.4723 nan 0.0100 0.0001
## 460 0.4628 nan 0.0100 0.0001
## 480 0.4538 nan 0.0100 -0.0002
## 500 0.4440 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3101 nan 0.0100 0.0048
## 2 1.3010 nan 0.0100 0.0042
## 3 1.2920 nan 0.0100 0.0044
## 4 1.2826 nan 0.0100 0.0042
## 5 1.2740 nan 0.0100 0.0037
## 6 1.2652 nan 0.0100 0.0038
## 7 1.2561 nan 0.0100 0.0039
## 8 1.2474 nan 0.0100 0.0039
## 9 1.2395 nan 0.0100 0.0034
## 10 1.2318 nan 0.0100 0.0035
## 20 1.1569 nan 0.0100 0.0031
## 40 1.0378 nan 0.0100 0.0018
## 60 0.9495 nan 0.0100 0.0015
## 80 0.8798 nan 0.0100 0.0014
## 100 0.8245 nan 0.0100 0.0012
## 120 0.7781 nan 0.0100 0.0007
## 140 0.7395 nan 0.0100 0.0005
## 160 0.7082 nan 0.0100 0.0004
## 180 0.6813 nan 0.0100 0.0003
## 200 0.6588 nan 0.0100 0.0003
## 220 0.6371 nan 0.0100 0.0002
## 240 0.6187 nan 0.0100 0.0000
## 260 0.6018 nan 0.0100 -0.0000
## 280 0.5861 nan 0.0100 -0.0000
## 300 0.5714 nan 0.0100 0.0002
## 320 0.5580 nan 0.0100 0.0001
## 340 0.5453 nan 0.0100 -0.0000
## 360 0.5342 nan 0.0100 -0.0001
## 380 0.5230 nan 0.0100 -0.0001
## 400 0.5118 nan 0.0100 -0.0001
## 420 0.5021 nan 0.0100 0.0000
## 440 0.4924 nan 0.0100 -0.0001
## 460 0.4824 nan 0.0100 -0.0001
## 480 0.4731 nan 0.0100 -0.0002
## 500 0.4643 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0402
## 2 1.1683 nan 0.1000 0.0305
## 3 1.1064 nan 0.1000 0.0307
## 4 1.0539 nan 0.1000 0.0215
## 5 1.0074 nan 0.1000 0.0200
## 6 0.9648 nan 0.1000 0.0186
## 7 0.9310 nan 0.1000 0.0101
## 8 0.8990 nan 0.1000 0.0128
## 9 0.8709 nan 0.1000 0.0105
## 10 0.8478 nan 0.1000 0.0081
## 20 0.6969 nan 0.1000 0.0007
## 40 0.5698 nan 0.1000 -0.0004
## 60 0.4949 nan 0.1000 -0.0005
## 80 0.4359 nan 0.1000 -0.0014
## 100 0.3805 nan 0.1000 -0.0002
## 120 0.3358 nan 0.1000 -0.0009
## 140 0.2991 nan 0.1000 -0.0004
## 160 0.2675 nan 0.1000 -0.0006
## 180 0.2436 nan 0.1000 -0.0001
## 200 0.2218 nan 0.1000 -0.0008
## 220 0.2019 nan 0.1000 -0.0002
## 240 0.1830 nan 0.1000 -0.0007
## 260 0.1668 nan 0.1000 -0.0003
## 280 0.1520 nan 0.1000 -0.0003
## 300 0.1393 nan 0.1000 -0.0002
## 320 0.1279 nan 0.1000 0.0001
## 340 0.1172 nan 0.1000 -0.0002
## 360 0.1090 nan 0.1000 -0.0003
## 380 0.1004 nan 0.1000 -0.0003
## 400 0.0925 nan 0.1000 -0.0005
## 420 0.0854 nan 0.1000 -0.0001
## 440 0.0790 nan 0.1000 -0.0001
## 460 0.0719 nan 0.1000 -0.0001
## 480 0.0666 nan 0.1000 -0.0001
## 500 0.0618 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2452 nan 0.1000 0.0344
## 2 1.1714 nan 0.1000 0.0347
## 3 1.1087 nan 0.1000 0.0291
## 4 1.0550 nan 0.1000 0.0196
## 5 1.0128 nan 0.1000 0.0177
## 6 0.9734 nan 0.1000 0.0161
## 7 0.9379 nan 0.1000 0.0167
## 8 0.9016 nan 0.1000 0.0128
## 9 0.8754 nan 0.1000 0.0115
## 10 0.8528 nan 0.1000 0.0087
## 20 0.7109 nan 0.1000 0.0021
## 40 0.5813 nan 0.1000 -0.0007
## 60 0.5016 nan 0.1000 -0.0004
## 80 0.4404 nan 0.1000 -0.0020
## 100 0.3875 nan 0.1000 -0.0006
## 120 0.3450 nan 0.1000 -0.0015
## 140 0.3048 nan 0.1000 -0.0002
## 160 0.2733 nan 0.1000 -0.0008
## 180 0.2487 nan 0.1000 -0.0018
## 200 0.2238 nan 0.1000 -0.0008
## 220 0.2045 nan 0.1000 -0.0004
## 240 0.1864 nan 0.1000 -0.0006
## 260 0.1684 nan 0.1000 -0.0005
## 280 0.1561 nan 0.1000 -0.0008
## 300 0.1433 nan 0.1000 -0.0005
## 320 0.1309 nan 0.1000 -0.0000
## 340 0.1211 nan 0.1000 -0.0005
## 360 0.1118 nan 0.1000 -0.0003
## 380 0.1042 nan 0.1000 -0.0001
## 400 0.0950 nan 0.1000 -0.0002
## 420 0.0874 nan 0.1000 -0.0004
## 440 0.0806 nan 0.1000 -0.0002
## 460 0.0754 nan 0.1000 -0.0004
## 480 0.0697 nan 0.1000 -0.0001
## 500 0.0641 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0428
## 2 1.1717 nan 0.1000 0.0274
## 3 1.1124 nan 0.1000 0.0236
## 4 1.0579 nan 0.1000 0.0236
## 5 1.0154 nan 0.1000 0.0185
## 6 0.9738 nan 0.1000 0.0169
## 7 0.9417 nan 0.1000 0.0107
## 8 0.9123 nan 0.1000 0.0134
## 9 0.8864 nan 0.1000 0.0087
## 10 0.8629 nan 0.1000 0.0087
## 20 0.7096 nan 0.1000 0.0013
## 40 0.5977 nan 0.1000 0.0009
## 60 0.5186 nan 0.1000 -0.0008
## 80 0.4521 nan 0.1000 -0.0000
## 100 0.3970 nan 0.1000 0.0006
## 120 0.3647 nan 0.1000 -0.0007
## 140 0.3262 nan 0.1000 -0.0002
## 160 0.2954 nan 0.1000 -0.0003
## 180 0.2703 nan 0.1000 -0.0012
## 200 0.2484 nan 0.1000 -0.0007
## 220 0.2282 nan 0.1000 -0.0007
## 240 0.2087 nan 0.1000 -0.0008
## 260 0.1905 nan 0.1000 0.0001
## 280 0.1750 nan 0.1000 -0.0002
## 300 0.1609 nan 0.1000 -0.0007
## 320 0.1478 nan 0.1000 -0.0005
## 340 0.1359 nan 0.1000 -0.0007
## 360 0.1254 nan 0.1000 -0.0005
## 380 0.1152 nan 0.1000 -0.0005
## 400 0.1061 nan 0.1000 -0.0005
## 420 0.0988 nan 0.1000 -0.0003
## 440 0.0914 nan 0.1000 -0.0003
## 460 0.0840 nan 0.1000 -0.0002
## 480 0.0779 nan 0.1000 -0.0003
## 500 0.0730 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2311 nan 0.1000 0.0346
## 2 1.1573 nan 0.1000 0.0330
## 3 1.0909 nan 0.1000 0.0297
## 4 1.0416 nan 0.1000 0.0221
## 5 0.9951 nan 0.1000 0.0192
## 6 0.9564 nan 0.1000 0.0166
## 7 0.9235 nan 0.1000 0.0141
## 8 0.8902 nan 0.1000 0.0141
## 9 0.8615 nan 0.1000 0.0109
## 10 0.8372 nan 0.1000 0.0093
## 20 0.6817 nan 0.1000 0.0005
## 40 0.5316 nan 0.1000 -0.0006
## 60 0.4421 nan 0.1000 -0.0015
## 80 0.3796 nan 0.1000 -0.0002
## 100 0.3264 nan 0.1000 -0.0002
## 120 0.2844 nan 0.1000 -0.0003
## 140 0.2426 nan 0.1000 -0.0008
## 160 0.2118 nan 0.1000 -0.0003
## 180 0.1916 nan 0.1000 0.0001
## 200 0.1707 nan 0.1000 -0.0003
## 220 0.1524 nan 0.1000 -0.0007
## 240 0.1344 nan 0.1000 -0.0004
## 260 0.1204 nan 0.1000 -0.0007
## 280 0.1065 nan 0.1000 -0.0001
## 300 0.0958 nan 0.1000 -0.0001
## 320 0.0862 nan 0.1000 -0.0002
## 340 0.0778 nan 0.1000 -0.0002
## 360 0.0700 nan 0.1000 -0.0003
## 380 0.0637 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 -0.0001
## 420 0.0524 nan 0.1000 -0.0000
## 440 0.0481 nan 0.1000 -0.0002
## 460 0.0431 nan 0.1000 -0.0000
## 480 0.0388 nan 0.1000 -0.0000
## 500 0.0355 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0405
## 2 1.1639 nan 0.1000 0.0324
## 3 1.1015 nan 0.1000 0.0281
## 4 1.0484 nan 0.1000 0.0210
## 5 1.0029 nan 0.1000 0.0201
## 6 0.9600 nan 0.1000 0.0159
## 7 0.9249 nan 0.1000 0.0154
## 8 0.8939 nan 0.1000 0.0134
## 9 0.8645 nan 0.1000 0.0122
## 10 0.8407 nan 0.1000 0.0108
## 20 0.6863 nan 0.1000 0.0041
## 40 0.5444 nan 0.1000 -0.0001
## 60 0.4535 nan 0.1000 -0.0010
## 80 0.3910 nan 0.1000 -0.0014
## 100 0.3413 nan 0.1000 -0.0005
## 120 0.2966 nan 0.1000 -0.0005
## 140 0.2612 nan 0.1000 -0.0007
## 160 0.2282 nan 0.1000 -0.0004
## 180 0.1996 nan 0.1000 -0.0006
## 200 0.1745 nan 0.1000 -0.0005
## 220 0.1546 nan 0.1000 -0.0006
## 240 0.1381 nan 0.1000 -0.0005
## 260 0.1232 nan 0.1000 -0.0001
## 280 0.1102 nan 0.1000 -0.0005
## 300 0.0978 nan 0.1000 0.0001
## 320 0.0879 nan 0.1000 -0.0002
## 340 0.0801 nan 0.1000 -0.0003
## 360 0.0724 nan 0.1000 -0.0003
## 380 0.0654 nan 0.1000 -0.0002
## 400 0.0589 nan 0.1000 -0.0002
## 420 0.0532 nan 0.1000 -0.0001
## 440 0.0486 nan 0.1000 -0.0003
## 460 0.0437 nan 0.1000 0.0000
## 480 0.0403 nan 0.1000 -0.0001
## 500 0.0364 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2275 nan 0.1000 0.0377
## 2 1.1582 nan 0.1000 0.0295
## 3 1.0990 nan 0.1000 0.0238
## 4 1.0547 nan 0.1000 0.0191
## 5 1.0019 nan 0.1000 0.0208
## 6 0.9658 nan 0.1000 0.0114
## 7 0.9262 nan 0.1000 0.0150
## 8 0.8923 nan 0.1000 0.0145
## 9 0.8662 nan 0.1000 0.0099
## 10 0.8400 nan 0.1000 0.0075
## 20 0.6911 nan 0.1000 0.0019
## 40 0.5566 nan 0.1000 -0.0012
## 60 0.4731 nan 0.1000 -0.0001
## 80 0.4049 nan 0.1000 -0.0008
## 100 0.3464 nan 0.1000 -0.0011
## 120 0.3010 nan 0.1000 -0.0011
## 140 0.2658 nan 0.1000 -0.0011
## 160 0.2350 nan 0.1000 -0.0005
## 180 0.2068 nan 0.1000 -0.0012
## 200 0.1848 nan 0.1000 -0.0012
## 220 0.1654 nan 0.1000 -0.0010
## 240 0.1479 nan 0.1000 -0.0007
## 260 0.1341 nan 0.1000 -0.0006
## 280 0.1201 nan 0.1000 -0.0006
## 300 0.1079 nan 0.1000 -0.0005
## 320 0.0977 nan 0.1000 -0.0003
## 340 0.0880 nan 0.1000 -0.0002
## 360 0.0803 nan 0.1000 -0.0005
## 380 0.0726 nan 0.1000 -0.0003
## 400 0.0662 nan 0.1000 -0.0004
## 420 0.0607 nan 0.1000 -0.0001
## 440 0.0556 nan 0.1000 -0.0002
## 460 0.0503 nan 0.1000 -0.0002
## 480 0.0463 nan 0.1000 -0.0002
## 500 0.0423 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2289 nan 0.1000 0.0437
## 2 1.1502 nan 0.1000 0.0301
## 3 1.0861 nan 0.1000 0.0285
## 4 1.0256 nan 0.1000 0.0237
## 5 0.9811 nan 0.1000 0.0183
## 6 0.9349 nan 0.1000 0.0198
## 7 0.8934 nan 0.1000 0.0185
## 8 0.8628 nan 0.1000 0.0128
## 9 0.8328 nan 0.1000 0.0098
## 10 0.8031 nan 0.1000 0.0119
## 20 0.6356 nan 0.1000 0.0029
## 40 0.4870 nan 0.1000 0.0005
## 60 0.3925 nan 0.1000 0.0001
## 80 0.3313 nan 0.1000 -0.0003
## 100 0.2798 nan 0.1000 -0.0001
## 120 0.2322 nan 0.1000 0.0006
## 140 0.1996 nan 0.1000 -0.0013
## 160 0.1687 nan 0.1000 -0.0006
## 180 0.1448 nan 0.1000 -0.0001
## 200 0.1269 nan 0.1000 -0.0006
## 220 0.1102 nan 0.1000 -0.0001
## 240 0.0964 nan 0.1000 -0.0003
## 260 0.0843 nan 0.1000 -0.0002
## 280 0.0740 nan 0.1000 -0.0000
## 300 0.0653 nan 0.1000 -0.0002
## 320 0.0575 nan 0.1000 -0.0000
## 340 0.0507 nan 0.1000 -0.0002
## 360 0.0448 nan 0.1000 -0.0001
## 380 0.0397 nan 0.1000 -0.0001
## 400 0.0353 nan 0.1000 -0.0001
## 420 0.0312 nan 0.1000 -0.0001
## 440 0.0278 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0000
## 480 0.0215 nan 0.1000 -0.0000
## 500 0.0191 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2175 nan 0.1000 0.0444
## 2 1.1461 nan 0.1000 0.0305
## 3 1.0787 nan 0.1000 0.0294
## 4 1.0245 nan 0.1000 0.0236
## 5 0.9767 nan 0.1000 0.0211
## 6 0.9341 nan 0.1000 0.0172
## 7 0.8926 nan 0.1000 0.0159
## 8 0.8619 nan 0.1000 0.0126
## 9 0.8333 nan 0.1000 0.0131
## 10 0.8083 nan 0.1000 0.0090
## 20 0.6507 nan 0.1000 0.0016
## 40 0.4908 nan 0.1000 -0.0004
## 60 0.4022 nan 0.1000 -0.0001
## 80 0.3316 nan 0.1000 -0.0000
## 100 0.2780 nan 0.1000 -0.0005
## 120 0.2377 nan 0.1000 -0.0003
## 140 0.2037 nan 0.1000 -0.0002
## 160 0.1751 nan 0.1000 -0.0008
## 180 0.1513 nan 0.1000 -0.0008
## 200 0.1304 nan 0.1000 -0.0004
## 220 0.1127 nan 0.1000 -0.0003
## 240 0.0980 nan 0.1000 -0.0001
## 260 0.0864 nan 0.1000 -0.0001
## 280 0.0754 nan 0.1000 -0.0003
## 300 0.0651 nan 0.1000 -0.0002
## 320 0.0576 nan 0.1000 -0.0002
## 340 0.0516 nan 0.1000 -0.0001
## 360 0.0453 nan 0.1000 -0.0002
## 380 0.0404 nan 0.1000 -0.0002
## 400 0.0360 nan 0.1000 -0.0002
## 420 0.0317 nan 0.1000 -0.0002
## 440 0.0283 nan 0.1000 -0.0001
## 460 0.0252 nan 0.1000 -0.0000
## 480 0.0223 nan 0.1000 -0.0000
## 500 0.0199 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.1000 0.0361
## 2 1.1628 nan 0.1000 0.0325
## 3 1.0930 nan 0.1000 0.0302
## 4 1.0431 nan 0.1000 0.0223
## 5 0.9887 nan 0.1000 0.0215
## 6 0.9467 nan 0.1000 0.0164
## 7 0.9116 nan 0.1000 0.0141
## 8 0.8762 nan 0.1000 0.0143
## 9 0.8489 nan 0.1000 0.0124
## 10 0.8211 nan 0.1000 0.0098
## 20 0.6559 nan 0.1000 0.0023
## 40 0.5206 nan 0.1000 -0.0008
## 60 0.4305 nan 0.1000 -0.0007
## 80 0.3595 nan 0.1000 -0.0017
## 100 0.3039 nan 0.1000 -0.0004
## 120 0.2624 nan 0.1000 -0.0014
## 140 0.2222 nan 0.1000 -0.0007
## 160 0.1925 nan 0.1000 -0.0005
## 180 0.1675 nan 0.1000 -0.0010
## 200 0.1462 nan 0.1000 -0.0004
## 220 0.1265 nan 0.1000 -0.0000
## 240 0.1107 nan 0.1000 -0.0005
## 260 0.0966 nan 0.1000 -0.0004
## 280 0.0851 nan 0.1000 -0.0004
## 300 0.0743 nan 0.1000 -0.0002
## 320 0.0654 nan 0.1000 -0.0002
## 340 0.0582 nan 0.1000 -0.0003
## 360 0.0516 nan 0.1000 -0.0002
## 380 0.0456 nan 0.1000 -0.0002
## 400 0.0409 nan 0.1000 -0.0002
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## 460 0.0297 nan 0.1000 -0.0002
## 480 0.0267 nan 0.1000 -0.0001
## 500 0.0240 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2883 nan 0.0010 0.0003
## 60 1.2726 nan 0.0010 0.0003
## 80 1.2579 nan 0.0010 0.0003
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2295 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2028 nan 0.0010 0.0003
## 180 1.1899 nan 0.0010 0.0003
## 200 1.1776 nan 0.0010 0.0003
## 220 1.1658 nan 0.0010 0.0003
## 240 1.1540 nan 0.0010 0.0002
## 260 1.1427 nan 0.0010 0.0002
## 280 1.1320 nan 0.0010 0.0002
## 300 1.1212 nan 0.0010 0.0002
## 320 1.1107 nan 0.0010 0.0002
## 340 1.1008 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0724 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
## 440 1.0545 nan 0.0010 0.0002
## 460 1.0458 nan 0.0010 0.0002
## 480 1.0374 nan 0.0010 0.0002
## 500 1.0292 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0003
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2731 nan 0.0010 0.0003
## 80 1.2581 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2298 nan 0.0010 0.0003
## 140 1.2165 nan 0.0010 0.0003
## 160 1.2033 nan 0.0010 0.0003
## 180 1.1906 nan 0.0010 0.0002
## 200 1.1781 nan 0.0010 0.0003
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1545 nan 0.0010 0.0002
## 260 1.1432 nan 0.0010 0.0003
## 280 1.1323 nan 0.0010 0.0002
## 300 1.1216 nan 0.0010 0.0002
## 320 1.1115 nan 0.0010 0.0002
## 340 1.1014 nan 0.0010 0.0002
## 360 1.0919 nan 0.0010 0.0002
## 380 1.0822 nan 0.0010 0.0002
## 400 1.0729 nan 0.0010 0.0002
## 420 1.0638 nan 0.0010 0.0002
## 440 1.0549 nan 0.0010 0.0002
## 460 1.0466 nan 0.0010 0.0002
## 480 1.0384 nan 0.0010 0.0002
## 500 1.0303 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2890 nan 0.0010 0.0004
## 60 1.2737 nan 0.0010 0.0004
## 80 1.2587 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2169 nan 0.0010 0.0003
## 160 1.2038 nan 0.0010 0.0003
## 180 1.1912 nan 0.0010 0.0003
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1671 nan 0.0010 0.0003
## 240 1.1554 nan 0.0010 0.0003
## 260 1.1442 nan 0.0010 0.0002
## 280 1.1330 nan 0.0010 0.0003
## 300 1.1225 nan 0.0010 0.0002
## 320 1.1124 nan 0.0010 0.0002
## 340 1.1024 nan 0.0010 0.0002
## 360 1.0927 nan 0.0010 0.0002
## 380 1.0833 nan 0.0010 0.0002
## 400 1.0740 nan 0.0010 0.0002
## 420 1.0652 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0479 nan 0.0010 0.0002
## 480 1.0397 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0003
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2533 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0003
## 120 1.2228 nan 0.0010 0.0004
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1950 nan 0.0010 0.0003
## 180 1.1815 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1555 nan 0.0010 0.0002
## 240 1.1433 nan 0.0010 0.0003
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## 280 1.1199 nan 0.0010 0.0002
## 300 1.1087 nan 0.0010 0.0002
## 320 1.0977 nan 0.0010 0.0002
## 340 1.0869 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0665 nan 0.0010 0.0002
## 400 1.0566 nan 0.0010 0.0002
## 420 1.0473 nan 0.0010 0.0002
## 440 1.0381 nan 0.0010 0.0002
## 460 1.0289 nan 0.0010 0.0002
## 480 1.0204 nan 0.0010 0.0001
## 500 1.0118 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2547 nan 0.0010 0.0004
## 100 1.2394 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2098 nan 0.0010 0.0003
## 160 1.1955 nan 0.0010 0.0003
## 180 1.1822 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1569 nan 0.0010 0.0003
## 240 1.1445 nan 0.0010 0.0002
## 260 1.1328 nan 0.0010 0.0002
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1102 nan 0.0010 0.0003
## 320 1.0993 nan 0.0010 0.0003
## 340 1.0887 nan 0.0010 0.0002
## 360 1.0784 nan 0.0010 0.0002
## 380 1.0685 nan 0.0010 0.0002
## 400 1.0589 nan 0.0010 0.0002
## 420 1.0494 nan 0.0010 0.0002
## 440 1.0403 nan 0.0010 0.0002
## 460 1.0312 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2868 nan 0.0010 0.0003
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2391 nan 0.0010 0.0004
## 120 1.2242 nan 0.0010 0.0003
## 140 1.2104 nan 0.0010 0.0003
## 160 1.1967 nan 0.0010 0.0003
## 180 1.1833 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1574 nan 0.0010 0.0003
## 240 1.1453 nan 0.0010 0.0003
## 260 1.1334 nan 0.0010 0.0003
## 280 1.1220 nan 0.0010 0.0002
## 300 1.1108 nan 0.0010 0.0002
## 320 1.1003 nan 0.0010 0.0002
## 340 1.0897 nan 0.0010 0.0002
## 360 1.0795 nan 0.0010 0.0002
## 380 1.0696 nan 0.0010 0.0002
## 400 1.0600 nan 0.0010 0.0001
## 420 1.0505 nan 0.0010 0.0002
## 440 1.0418 nan 0.0010 0.0002
## 460 1.0329 nan 0.0010 0.0002
## 480 1.0242 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2346 nan 0.0010 0.0003
## 120 1.2188 nan 0.0010 0.0004
## 140 1.2040 nan 0.0010 0.0003
## 160 1.1896 nan 0.0010 0.0003
## 180 1.1754 nan 0.0010 0.0003
## 200 1.1620 nan 0.0010 0.0003
## 220 1.1487 nan 0.0010 0.0003
## 240 1.1357 nan 0.0010 0.0003
## 260 1.1232 nan 0.0010 0.0003
## 280 1.1109 nan 0.0010 0.0003
## 300 1.0993 nan 0.0010 0.0002
## 320 1.0879 nan 0.0010 0.0002
## 340 1.0768 nan 0.0010 0.0002
## 360 1.0660 nan 0.0010 0.0002
## 380 1.0553 nan 0.0010 0.0002
## 400 1.0454 nan 0.0010 0.0002
## 420 1.0351 nan 0.0010 0.0002
## 440 1.0257 nan 0.0010 0.0002
## 460 1.0164 nan 0.0010 0.0002
## 480 1.0071 nan 0.0010 0.0002
## 500 0.9981 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0003
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0002
## 200 1.1635 nan 0.0010 0.0003
## 220 1.1504 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0003
## 260 1.1256 nan 0.0010 0.0002
## 280 1.1136 nan 0.0010 0.0003
## 300 1.1018 nan 0.0010 0.0002
## 320 1.0907 nan 0.0010 0.0002
## 340 1.0796 nan 0.0010 0.0002
## 360 1.0688 nan 0.0010 0.0002
## 380 1.0582 nan 0.0010 0.0002
## 400 1.0480 nan 0.0010 0.0002
## 420 1.0380 nan 0.0010 0.0002
## 440 1.0288 nan 0.0010 0.0001
## 460 1.0195 nan 0.0010 0.0002
## 480 1.0103 nan 0.0010 0.0002
## 500 1.0014 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2689 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0004
## 100 1.2366 nan 0.0010 0.0004
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1930 nan 0.0010 0.0003
## 180 1.1790 nan 0.0010 0.0003
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1525 nan 0.0010 0.0003
## 240 1.1398 nan 0.0010 0.0003
## 260 1.1276 nan 0.0010 0.0002
## 280 1.1159 nan 0.0010 0.0003
## 300 1.1042 nan 0.0010 0.0002
## 320 1.0932 nan 0.0010 0.0002
## 340 1.0823 nan 0.0010 0.0002
## 360 1.0716 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0511 nan 0.0010 0.0002
## 420 1.0413 nan 0.0010 0.0002
## 440 1.0319 nan 0.0010 0.0002
## 460 1.0226 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
## 500 1.0046 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0041
## 2 1.3038 nan 0.0100 0.0041
## 3 1.2962 nan 0.0100 0.0037
## 4 1.2886 nan 0.0100 0.0038
## 5 1.2808 nan 0.0100 0.0035
## 6 1.2729 nan 0.0100 0.0033
## 7 1.2654 nan 0.0100 0.0036
## 8 1.2583 nan 0.0100 0.0031
## 9 1.2515 nan 0.0100 0.0032
## 10 1.2439 nan 0.0100 0.0030
## 20 1.1763 nan 0.0100 0.0027
## 40 1.0718 nan 0.0100 0.0018
## 60 0.9913 nan 0.0100 0.0016
## 80 0.9285 nan 0.0100 0.0011
## 100 0.8780 nan 0.0100 0.0008
## 120 0.8357 nan 0.0100 0.0006
## 140 0.8009 nan 0.0100 0.0007
## 160 0.7715 nan 0.0100 0.0004
## 180 0.7453 nan 0.0100 0.0000
## 200 0.7212 nan 0.0100 0.0002
## 220 0.7009 nan 0.0100 0.0003
## 240 0.6842 nan 0.0100 0.0000
## 260 0.6690 nan 0.0100 0.0001
## 280 0.6551 nan 0.0100 -0.0000
## 300 0.6419 nan 0.0100 -0.0001
## 320 0.6281 nan 0.0100 0.0000
## 340 0.6169 nan 0.0100 0.0002
## 360 0.6054 nan 0.0100 -0.0000
## 380 0.5947 nan 0.0100 -0.0000
## 400 0.5846 nan 0.0100 -0.0001
## 420 0.5760 nan 0.0100 -0.0002
## 440 0.5679 nan 0.0100 -0.0000
## 460 0.5596 nan 0.0100 0.0001
## 480 0.5514 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0042
## 4 1.2882 nan 0.0100 0.0031
## 5 1.2804 nan 0.0100 0.0037
## 6 1.2726 nan 0.0100 0.0035
## 7 1.2653 nan 0.0100 0.0031
## 8 1.2584 nan 0.0100 0.0031
## 9 1.2505 nan 0.0100 0.0037
## 10 1.2425 nan 0.0100 0.0037
## 20 1.1771 nan 0.0100 0.0025
## 40 1.0711 nan 0.0100 0.0020
## 60 0.9923 nan 0.0100 0.0016
## 80 0.9283 nan 0.0100 0.0011
## 100 0.8784 nan 0.0100 0.0009
## 120 0.8380 nan 0.0100 0.0007
## 140 0.8025 nan 0.0100 0.0006
## 160 0.7744 nan 0.0100 0.0005
## 180 0.7507 nan 0.0100 0.0002
## 200 0.7281 nan 0.0100 0.0004
## 220 0.7092 nan 0.0100 0.0003
## 240 0.6921 nan 0.0100 -0.0001
## 260 0.6761 nan 0.0100 0.0001
## 280 0.6629 nan 0.0100 0.0001
## 300 0.6511 nan 0.0100 -0.0001
## 320 0.6389 nan 0.0100 0.0002
## 340 0.6284 nan 0.0100 0.0000
## 360 0.6178 nan 0.0100 -0.0000
## 380 0.6074 nan 0.0100 0.0000
## 400 0.5973 nan 0.0100 -0.0001
## 420 0.5882 nan 0.0100 0.0001
## 440 0.5794 nan 0.0100 -0.0000
## 460 0.5701 nan 0.0100 -0.0002
## 480 0.5614 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0038
## 2 1.3044 nan 0.0100 0.0041
## 3 1.2966 nan 0.0100 0.0037
## 4 1.2888 nan 0.0100 0.0036
## 5 1.2811 nan 0.0100 0.0039
## 6 1.2742 nan 0.0100 0.0033
## 7 1.2660 nan 0.0100 0.0034
## 8 1.2586 nan 0.0100 0.0035
## 9 1.2512 nan 0.0100 0.0032
## 10 1.2438 nan 0.0100 0.0035
## 20 1.1781 nan 0.0100 0.0029
## 40 1.0728 nan 0.0100 0.0020
## 60 0.9935 nan 0.0100 0.0013
## 80 0.9303 nan 0.0100 0.0010
## 100 0.8809 nan 0.0100 0.0007
## 120 0.8390 nan 0.0100 0.0006
## 140 0.8059 nan 0.0100 0.0004
## 160 0.7776 nan 0.0100 0.0003
## 180 0.7527 nan 0.0100 0.0004
## 200 0.7325 nan 0.0100 0.0000
## 220 0.7147 nan 0.0100 -0.0000
## 240 0.6977 nan 0.0100 0.0002
## 260 0.6821 nan 0.0100 0.0002
## 280 0.6685 nan 0.0100 -0.0000
## 300 0.6551 nan 0.0100 0.0001
## 320 0.6427 nan 0.0100 -0.0000
## 340 0.6313 nan 0.0100 0.0001
## 360 0.6206 nan 0.0100 0.0000
## 380 0.6099 nan 0.0100 -0.0000
## 400 0.6006 nan 0.0100 -0.0000
## 420 0.5904 nan 0.0100 -0.0000
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## 460 0.5724 nan 0.0100 0.0000
## 480 0.5641 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0043
## 2 1.3030 nan 0.0100 0.0040
## 3 1.2947 nan 0.0100 0.0040
## 4 1.2858 nan 0.0100 0.0040
## 5 1.2779 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0032
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2532 nan 0.0100 0.0032
## 9 1.2452 nan 0.0100 0.0040
## 10 1.2376 nan 0.0100 0.0032
## 20 1.1686 nan 0.0100 0.0026
## 40 1.0588 nan 0.0100 0.0022
## 60 0.9743 nan 0.0100 0.0015
## 80 0.9081 nan 0.0100 0.0011
## 100 0.8547 nan 0.0100 0.0008
## 120 0.8125 nan 0.0100 0.0005
## 140 0.7767 nan 0.0100 0.0005
## 160 0.7465 nan 0.0100 0.0005
## 180 0.7190 nan 0.0100 0.0003
## 200 0.6944 nan 0.0100 0.0002
## 220 0.6737 nan 0.0100 0.0000
## 240 0.6536 nan 0.0100 0.0002
## 260 0.6349 nan 0.0100 0.0001
## 280 0.6188 nan 0.0100 0.0001
## 300 0.6038 nan 0.0100 -0.0000
## 320 0.5890 nan 0.0100 0.0001
## 340 0.5763 nan 0.0100 0.0001
## 360 0.5633 nan 0.0100 0.0000
## 380 0.5514 nan 0.0100 -0.0000
## 400 0.5397 nan 0.0100 0.0002
## 420 0.5289 nan 0.0100 -0.0000
## 440 0.5183 nan 0.0100 0.0000
## 460 0.5091 nan 0.0100 -0.0000
## 480 0.4999 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0043
## 2 1.3025 nan 0.0100 0.0034
## 3 1.2936 nan 0.0100 0.0039
## 4 1.2851 nan 0.0100 0.0038
## 5 1.2774 nan 0.0100 0.0037
## 6 1.2693 nan 0.0100 0.0034
## 7 1.2623 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0036
## 9 1.2467 nan 0.0100 0.0028
## 10 1.2392 nan 0.0100 0.0035
## 20 1.1690 nan 0.0100 0.0024
## 40 1.0565 nan 0.0100 0.0022
## 60 0.9728 nan 0.0100 0.0017
## 80 0.9069 nan 0.0100 0.0012
## 100 0.8544 nan 0.0100 0.0011
## 120 0.8105 nan 0.0100 0.0008
## 140 0.7735 nan 0.0100 0.0006
## 160 0.7444 nan 0.0100 0.0003
## 180 0.7180 nan 0.0100 0.0004
## 200 0.6958 nan 0.0100 0.0001
## 220 0.6746 nan 0.0100 0.0004
## 240 0.6551 nan 0.0100 0.0002
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## 280 0.6207 nan 0.0100 0.0001
## 300 0.6065 nan 0.0100 0.0001
## 320 0.5940 nan 0.0100 0.0000
## 340 0.5823 nan 0.0100 -0.0001
## 360 0.5699 nan 0.0100 0.0000
## 380 0.5584 nan 0.0100 -0.0003
## 400 0.5466 nan 0.0100 -0.0000
## 420 0.5361 nan 0.0100 -0.0001
## 440 0.5250 nan 0.0100 0.0001
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## 480 0.5059 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0038
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2956 nan 0.0100 0.0038
## 4 1.2872 nan 0.0100 0.0041
## 5 1.2791 nan 0.0100 0.0032
## 6 1.2708 nan 0.0100 0.0041
## 7 1.2630 nan 0.0100 0.0037
## 8 1.2552 nan 0.0100 0.0036
## 9 1.2474 nan 0.0100 0.0035
## 10 1.2402 nan 0.0100 0.0033
## 20 1.1727 nan 0.0100 0.0030
## 40 1.0626 nan 0.0100 0.0019
## 60 0.9786 nan 0.0100 0.0016
## 80 0.9124 nan 0.0100 0.0013
## 100 0.8596 nan 0.0100 0.0009
## 120 0.8179 nan 0.0100 0.0006
## 140 0.7824 nan 0.0100 0.0008
## 160 0.7506 nan 0.0100 0.0002
## 180 0.7251 nan 0.0100 0.0003
## 200 0.7030 nan 0.0100 0.0002
## 220 0.6840 nan 0.0100 0.0001
## 240 0.6653 nan 0.0100 -0.0000
## 260 0.6498 nan 0.0100 -0.0001
## 280 0.6339 nan 0.0100 -0.0001
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## 320 0.6049 nan 0.0100 0.0001
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## 360 0.5802 nan 0.0100 -0.0000
## 380 0.5674 nan 0.0100 0.0001
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## 420 0.5455 nan 0.0100 0.0001
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## 460 0.5257 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0040
## 3 1.2929 nan 0.0100 0.0039
## 4 1.2840 nan 0.0100 0.0039
## 5 1.2758 nan 0.0100 0.0037
## 6 1.2671 nan 0.0100 0.0039
## 7 1.2582 nan 0.0100 0.0040
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2416 nan 0.0100 0.0040
## 10 1.2337 nan 0.0100 0.0037
## 20 1.1617 nan 0.0100 0.0026
## 40 1.0445 nan 0.0100 0.0021
## 60 0.9548 nan 0.0100 0.0017
## 80 0.8844 nan 0.0100 0.0012
## 100 0.8296 nan 0.0100 0.0009
## 120 0.7843 nan 0.0100 0.0007
## 140 0.7457 nan 0.0100 0.0007
## 160 0.7120 nan 0.0100 0.0002
## 180 0.6846 nan 0.0100 0.0003
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## 240 0.6174 nan 0.0100 0.0002
## 260 0.5983 nan 0.0100 -0.0001
## 280 0.5816 nan 0.0100 0.0000
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## 340 0.5360 nan 0.0100 0.0001
## 360 0.5237 nan 0.0100 -0.0002
## 380 0.5117 nan 0.0100 -0.0000
## 400 0.4998 nan 0.0100 0.0001
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## 460 0.4660 nan 0.0100 -0.0001
## 480 0.4559 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2935 nan 0.0100 0.0042
## 4 1.2843 nan 0.0100 0.0040
## 5 1.2758 nan 0.0100 0.0038
## 6 1.2676 nan 0.0100 0.0038
## 7 1.2590 nan 0.0100 0.0038
## 8 1.2512 nan 0.0100 0.0035
## 9 1.2435 nan 0.0100 0.0036
## 10 1.2353 nan 0.0100 0.0034
## 20 1.1646 nan 0.0100 0.0029
## 40 1.0499 nan 0.0100 0.0020
## 60 0.9592 nan 0.0100 0.0019
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## 140 0.7505 nan 0.0100 0.0004
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## 380 0.5129 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3029 nan 0.0100 0.0039
## 3 1.2944 nan 0.0100 0.0042
## 4 1.2860 nan 0.0100 0.0038
## 5 1.2781 nan 0.0100 0.0035
## 6 1.2698 nan 0.0100 0.0038
## 7 1.2618 nan 0.0100 0.0033
## 8 1.2531 nan 0.0100 0.0038
## 9 1.2456 nan 0.0100 0.0035
## 10 1.2373 nan 0.0100 0.0037
## 20 1.1674 nan 0.0100 0.0030
## 40 1.0538 nan 0.0100 0.0023
## 60 0.9659 nan 0.0100 0.0013
## 80 0.8987 nan 0.0100 0.0011
## 100 0.8445 nan 0.0100 0.0009
## 120 0.7980 nan 0.0100 0.0007
## 140 0.7615 nan 0.0100 0.0005
## 160 0.7286 nan 0.0100 0.0003
## 180 0.7021 nan 0.0100 0.0002
## 200 0.6769 nan 0.0100 0.0002
## 220 0.6554 nan 0.0100 0.0002
## 240 0.6359 nan 0.0100 -0.0000
## 260 0.6197 nan 0.0100 -0.0000
## 280 0.6027 nan 0.0100 0.0000
## 300 0.5876 nan 0.0100 -0.0000
## 320 0.5724 nan 0.0100 0.0001
## 340 0.5576 nan 0.0100 -0.0000
## 360 0.5443 nan 0.0100 -0.0001
## 380 0.5325 nan 0.0100 -0.0001
## 400 0.5207 nan 0.0100 -0.0002
## 420 0.5083 nan 0.0100 -0.0001
## 440 0.4977 nan 0.0100 -0.0000
## 460 0.4871 nan 0.0100 0.0000
## 480 0.4768 nan 0.0100 -0.0001
## 500 0.4661 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0323
## 2 1.1750 nan 0.1000 0.0320
## 3 1.1131 nan 0.1000 0.0266
## 4 1.0713 nan 0.1000 0.0193
## 5 1.0296 nan 0.1000 0.0192
## 6 0.9890 nan 0.1000 0.0195
## 7 0.9552 nan 0.1000 0.0153
## 8 0.9293 nan 0.1000 0.0092
## 9 0.9025 nan 0.1000 0.0100
## 10 0.8796 nan 0.1000 0.0091
## 20 0.7259 nan 0.1000 0.0023
## 40 0.5947 nan 0.1000 -0.0019
## 60 0.5178 nan 0.1000 0.0001
## 80 0.4572 nan 0.1000 -0.0007
## 100 0.3960 nan 0.1000 -0.0011
## 120 0.3477 nan 0.1000 -0.0003
## 140 0.3100 nan 0.1000 0.0000
## 160 0.2745 nan 0.1000 -0.0004
## 180 0.2487 nan 0.1000 -0.0005
## 200 0.2226 nan 0.1000 -0.0001
## 220 0.2020 nan 0.1000 -0.0006
## 240 0.1840 nan 0.1000 -0.0003
## 260 0.1668 nan 0.1000 -0.0004
## 280 0.1514 nan 0.1000 -0.0009
## 300 0.1379 nan 0.1000 -0.0000
## 320 0.1267 nan 0.1000 -0.0001
## 340 0.1159 nan 0.1000 -0.0004
## 360 0.1077 nan 0.1000 -0.0001
## 380 0.0989 nan 0.1000 -0.0004
## 400 0.0913 nan 0.1000 -0.0001
## 420 0.0841 nan 0.1000 -0.0003
## 440 0.0772 nan 0.1000 -0.0002
## 460 0.0715 nan 0.1000 -0.0002
## 480 0.0662 nan 0.1000 -0.0002
## 500 0.0609 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2395 nan 0.1000 0.0399
## 2 1.1757 nan 0.1000 0.0288
## 3 1.1171 nan 0.1000 0.0281
## 4 1.0722 nan 0.1000 0.0170
## 5 1.0259 nan 0.1000 0.0180
## 6 0.9897 nan 0.1000 0.0160
## 7 0.9586 nan 0.1000 0.0125
## 8 0.9313 nan 0.1000 0.0105
## 9 0.9054 nan 0.1000 0.0099
## 10 0.8820 nan 0.1000 0.0101
## 20 0.7385 nan 0.1000 0.0043
## 40 0.6000 nan 0.1000 0.0006
## 60 0.5178 nan 0.1000 -0.0007
## 80 0.4477 nan 0.1000 -0.0006
## 100 0.3967 nan 0.1000 -0.0001
## 120 0.3512 nan 0.1000 -0.0007
## 140 0.3130 nan 0.1000 0.0005
## 160 0.2814 nan 0.1000 -0.0004
## 180 0.2520 nan 0.1000 -0.0008
## 200 0.2297 nan 0.1000 -0.0003
## 220 0.2086 nan 0.1000 -0.0004
## 240 0.1879 nan 0.1000 -0.0010
## 260 0.1721 nan 0.1000 -0.0001
## 280 0.1577 nan 0.1000 -0.0006
## 300 0.1420 nan 0.1000 0.0002
## 320 0.1299 nan 0.1000 -0.0005
## 340 0.1189 nan 0.1000 -0.0005
## 360 0.1092 nan 0.1000 -0.0002
## 380 0.1006 nan 0.1000 -0.0003
## 400 0.0925 nan 0.1000 -0.0004
## 420 0.0856 nan 0.1000 -0.0002
## 440 0.0787 nan 0.1000 -0.0002
## 460 0.0735 nan 0.1000 -0.0001
## 480 0.0674 nan 0.1000 -0.0003
## 500 0.0625 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2435 nan 0.1000 0.0367
## 2 1.1748 nan 0.1000 0.0324
## 3 1.1148 nan 0.1000 0.0269
## 4 1.0647 nan 0.1000 0.0212
## 5 1.0249 nan 0.1000 0.0189
## 6 0.9871 nan 0.1000 0.0142
## 7 0.9521 nan 0.1000 0.0132
## 8 0.9261 nan 0.1000 0.0117
## 9 0.9022 nan 0.1000 0.0087
## 10 0.8790 nan 0.1000 0.0086
## 20 0.7405 nan 0.1000 0.0023
## 40 0.6139 nan 0.1000 0.0003
## 60 0.5259 nan 0.1000 -0.0002
## 80 0.4583 nan 0.1000 -0.0035
## 100 0.4020 nan 0.1000 -0.0013
## 120 0.3589 nan 0.1000 -0.0002
## 140 0.3239 nan 0.1000 -0.0005
## 160 0.2899 nan 0.1000 -0.0003
## 180 0.2637 nan 0.1000 -0.0015
## 200 0.2421 nan 0.1000 -0.0002
## 220 0.2192 nan 0.1000 -0.0005
## 240 0.2013 nan 0.1000 -0.0003
## 260 0.1845 nan 0.1000 -0.0015
## 280 0.1700 nan 0.1000 -0.0008
## 300 0.1557 nan 0.1000 -0.0005
## 320 0.1421 nan 0.1000 -0.0001
## 340 0.1305 nan 0.1000 -0.0003
## 360 0.1197 nan 0.1000 -0.0002
## 380 0.1101 nan 0.1000 -0.0004
## 400 0.1017 nan 0.1000 -0.0002
## 420 0.0931 nan 0.1000 -0.0001
## 440 0.0861 nan 0.1000 -0.0003
## 460 0.0804 nan 0.1000 -0.0001
## 480 0.0748 nan 0.1000 -0.0004
## 500 0.0696 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0411
## 2 1.1648 nan 0.1000 0.0341
## 3 1.1033 nan 0.1000 0.0247
## 4 1.0479 nan 0.1000 0.0253
## 5 1.0049 nan 0.1000 0.0194
## 6 0.9622 nan 0.1000 0.0169
## 7 0.9194 nan 0.1000 0.0167
## 8 0.8908 nan 0.1000 0.0116
## 9 0.8624 nan 0.1000 0.0108
## 10 0.8373 nan 0.1000 0.0097
## 20 0.6821 nan 0.1000 0.0033
## 40 0.5384 nan 0.1000 -0.0002
## 60 0.4549 nan 0.1000 -0.0012
## 80 0.3852 nan 0.1000 -0.0000
## 100 0.3334 nan 0.1000 -0.0009
## 120 0.2869 nan 0.1000 0.0003
## 140 0.2481 nan 0.1000 -0.0005
## 160 0.2219 nan 0.1000 -0.0002
## 180 0.1955 nan 0.1000 -0.0006
## 200 0.1733 nan 0.1000 -0.0005
## 220 0.1526 nan 0.1000 -0.0006
## 240 0.1360 nan 0.1000 -0.0006
## 260 0.1209 nan 0.1000 -0.0003
## 280 0.1078 nan 0.1000 0.0001
## 300 0.0966 nan 0.1000 -0.0002
## 320 0.0868 nan 0.1000 -0.0001
## 340 0.0778 nan 0.1000 0.0000
## 360 0.0704 nan 0.1000 -0.0003
## 380 0.0637 nan 0.1000 -0.0001
## 400 0.0572 nan 0.1000 -0.0003
## 420 0.0517 nan 0.1000 -0.0001
## 440 0.0468 nan 0.1000 -0.0002
## 460 0.0423 nan 0.1000 -0.0001
## 480 0.0382 nan 0.1000 -0.0001
## 500 0.0345 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0384
## 2 1.1680 nan 0.1000 0.0286
## 3 1.1099 nan 0.1000 0.0265
## 4 1.0536 nan 0.1000 0.0247
## 5 1.0125 nan 0.1000 0.0151
## 6 0.9712 nan 0.1000 0.0152
## 7 0.9370 nan 0.1000 0.0130
## 8 0.9120 nan 0.1000 0.0089
## 9 0.8827 nan 0.1000 0.0116
## 10 0.8608 nan 0.1000 0.0068
## 20 0.6968 nan 0.1000 0.0042
## 40 0.5513 nan 0.1000 -0.0010
## 60 0.4513 nan 0.1000 -0.0012
## 80 0.3860 nan 0.1000 -0.0006
## 100 0.3337 nan 0.1000 0.0001
## 120 0.2919 nan 0.1000 -0.0006
## 140 0.2551 nan 0.1000 -0.0002
## 160 0.2250 nan 0.1000 -0.0006
## 180 0.1968 nan 0.1000 0.0001
## 200 0.1733 nan 0.1000 -0.0005
## 220 0.1529 nan 0.1000 -0.0002
## 240 0.1364 nan 0.1000 -0.0006
## 260 0.1216 nan 0.1000 -0.0007
## 280 0.1079 nan 0.1000 -0.0006
## 300 0.0961 nan 0.1000 -0.0004
## 320 0.0871 nan 0.1000 -0.0003
## 340 0.0784 nan 0.1000 -0.0003
## 360 0.0706 nan 0.1000 -0.0002
## 380 0.0630 nan 0.1000 -0.0002
## 400 0.0567 nan 0.1000 -0.0002
## 420 0.0512 nan 0.1000 -0.0003
## 440 0.0462 nan 0.1000 -0.0002
## 460 0.0414 nan 0.1000 -0.0001
## 480 0.0376 nan 0.1000 -0.0002
## 500 0.0342 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0397
## 2 1.1721 nan 0.1000 0.0324
## 3 1.1092 nan 0.1000 0.0276
## 4 1.0638 nan 0.1000 0.0190
## 5 1.0175 nan 0.1000 0.0200
## 6 0.9738 nan 0.1000 0.0183
## 7 0.9409 nan 0.1000 0.0139
## 8 0.9147 nan 0.1000 0.0108
## 9 0.8812 nan 0.1000 0.0120
## 10 0.8610 nan 0.1000 0.0057
## 20 0.7164 nan 0.1000 0.0003
## 40 0.5645 nan 0.1000 0.0010
## 60 0.4733 nan 0.1000 -0.0006
## 80 0.4006 nan 0.1000 -0.0012
## 100 0.3473 nan 0.1000 -0.0009
## 120 0.2997 nan 0.1000 -0.0005
## 140 0.2608 nan 0.1000 -0.0007
## 160 0.2296 nan 0.1000 -0.0007
## 180 0.2020 nan 0.1000 -0.0009
## 200 0.1791 nan 0.1000 -0.0003
## 220 0.1607 nan 0.1000 -0.0006
## 240 0.1468 nan 0.1000 -0.0007
## 260 0.1314 nan 0.1000 -0.0009
## 280 0.1169 nan 0.1000 -0.0006
## 300 0.1056 nan 0.1000 -0.0006
## 320 0.0950 nan 0.1000 -0.0002
## 340 0.0866 nan 0.1000 -0.0001
## 360 0.0776 nan 0.1000 -0.0003
## 380 0.0696 nan 0.1000 -0.0002
## 400 0.0621 nan 0.1000 -0.0001
## 420 0.0566 nan 0.1000 -0.0003
## 440 0.0512 nan 0.1000 -0.0002
## 460 0.0469 nan 0.1000 -0.0001
## 480 0.0428 nan 0.1000 -0.0002
## 500 0.0386 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0413
## 2 1.1617 nan 0.1000 0.0335
## 3 1.0931 nan 0.1000 0.0300
## 4 1.0370 nan 0.1000 0.0244
## 5 0.9901 nan 0.1000 0.0185
## 6 0.9502 nan 0.1000 0.0160
## 7 0.9167 nan 0.1000 0.0121
## 8 0.8878 nan 0.1000 0.0121
## 9 0.8572 nan 0.1000 0.0100
## 10 0.8331 nan 0.1000 0.0091
## 20 0.6635 nan 0.1000 0.0023
## 40 0.4998 nan 0.1000 -0.0000
## 60 0.3983 nan 0.1000 0.0005
## 80 0.3293 nan 0.1000 -0.0002
## 100 0.2756 nan 0.1000 -0.0012
## 120 0.2282 nan 0.1000 0.0004
## 140 0.1934 nan 0.1000 -0.0001
## 160 0.1650 nan 0.1000 -0.0001
## 180 0.1417 nan 0.1000 -0.0004
## 200 0.1230 nan 0.1000 -0.0000
## 220 0.1077 nan 0.1000 -0.0004
## 240 0.0924 nan 0.1000 -0.0001
## 260 0.0798 nan 0.1000 -0.0003
## 280 0.0705 nan 0.1000 -0.0003
## 300 0.0615 nan 0.1000 -0.0001
## 320 0.0538 nan 0.1000 -0.0001
## 340 0.0476 nan 0.1000 -0.0001
## 360 0.0416 nan 0.1000 -0.0000
## 380 0.0371 nan 0.1000 -0.0002
## 400 0.0322 nan 0.1000 -0.0001
## 420 0.0286 nan 0.1000 -0.0001
## 440 0.0251 nan 0.1000 -0.0000
## 460 0.0223 nan 0.1000 -0.0001
## 480 0.0199 nan 0.1000 -0.0001
## 500 0.0177 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0419
## 2 1.1647 nan 0.1000 0.0313
## 3 1.0931 nan 0.1000 0.0303
## 4 1.0383 nan 0.1000 0.0237
## 5 0.9897 nan 0.1000 0.0187
## 6 0.9503 nan 0.1000 0.0172
## 7 0.9139 nan 0.1000 0.0148
## 8 0.8850 nan 0.1000 0.0128
## 9 0.8597 nan 0.1000 0.0100
## 10 0.8364 nan 0.1000 0.0082
## 20 0.6719 nan 0.1000 0.0023
## 40 0.5142 nan 0.1000 -0.0006
## 60 0.4163 nan 0.1000 -0.0017
## 80 0.3377 nan 0.1000 -0.0001
## 100 0.2817 nan 0.1000 -0.0006
## 120 0.2382 nan 0.1000 -0.0005
## 140 0.2049 nan 0.1000 -0.0006
## 160 0.1765 nan 0.1000 -0.0006
## 180 0.1523 nan 0.1000 0.0000
## 200 0.1327 nan 0.1000 -0.0001
## 220 0.1147 nan 0.1000 -0.0004
## 240 0.0991 nan 0.1000 -0.0001
## 260 0.0877 nan 0.1000 -0.0003
## 280 0.0766 nan 0.1000 -0.0003
## 300 0.0675 nan 0.1000 -0.0002
## 320 0.0590 nan 0.1000 -0.0001
## 340 0.0518 nan 0.1000 -0.0001
## 360 0.0453 nan 0.1000 -0.0002
## 380 0.0400 nan 0.1000 -0.0001
## 400 0.0353 nan 0.1000 -0.0001
## 420 0.0310 nan 0.1000 -0.0001
## 440 0.0274 nan 0.1000 -0.0002
## 460 0.0246 nan 0.1000 -0.0001
## 480 0.0216 nan 0.1000 -0.0000
## 500 0.0189 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0409
## 2 1.1600 nan 0.1000 0.0307
## 3 1.1024 nan 0.1000 0.0234
## 4 1.0550 nan 0.1000 0.0209
## 5 1.0126 nan 0.1000 0.0209
## 6 0.9738 nan 0.1000 0.0187
## 7 0.9351 nan 0.1000 0.0138
## 8 0.8949 nan 0.1000 0.0145
## 9 0.8679 nan 0.1000 0.0086
## 10 0.8426 nan 0.1000 0.0082
## 20 0.6752 nan 0.1000 0.0016
## 40 0.5241 nan 0.1000 0.0014
## 60 0.4196 nan 0.1000 -0.0003
## 80 0.3414 nan 0.1000 -0.0000
## 100 0.2917 nan 0.1000 -0.0008
## 120 0.2469 nan 0.1000 -0.0007
## 140 0.2133 nan 0.1000 -0.0006
## 160 0.1847 nan 0.1000 -0.0005
## 180 0.1596 nan 0.1000 -0.0011
## 200 0.1392 nan 0.1000 -0.0003
## 220 0.1205 nan 0.1000 -0.0006
## 240 0.1047 nan 0.1000 -0.0009
## 260 0.0905 nan 0.1000 -0.0003
## 280 0.0793 nan 0.1000 -0.0003
## 300 0.0696 nan 0.1000 -0.0002
## 320 0.0610 nan 0.1000 -0.0000
## 340 0.0539 nan 0.1000 -0.0002
## 360 0.0475 nan 0.1000 -0.0002
## 380 0.0421 nan 0.1000 -0.0001
## 400 0.0370 nan 0.1000 -0.0003
## 420 0.0324 nan 0.1000 -0.0002
## 440 0.0284 nan 0.1000 -0.0001
## 460 0.0255 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0000
## 500 0.0198 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0003
## 40 1.2861 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2240 nan 0.0010 0.0003
## 140 1.2094 nan 0.0010 0.0003
## 160 1.1958 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1694 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0003
## 260 1.1322 nan 0.0010 0.0002
## 280 1.1201 nan 0.0010 0.0003
## 300 1.1089 nan 0.0010 0.0002
## 320 1.0980 nan 0.0010 0.0002
## 340 1.0876 nan 0.0010 0.0002
## 360 1.0773 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0577 nan 0.0010 0.0002
## 420 1.0481 nan 0.0010 0.0002
## 440 1.0392 nan 0.0010 0.0002
## 460 1.0300 nan 0.0010 0.0002
## 480 1.0216 nan 0.0010 0.0002
## 500 1.0130 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0003
## 40 1.2859 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0003
## 80 1.2536 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1954 nan 0.0010 0.0003
## 180 1.1821 nan 0.0010 0.0002
## 200 1.1688 nan 0.0010 0.0003
## 220 1.1564 nan 0.0010 0.0003
## 240 1.1443 nan 0.0010 0.0002
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1211 nan 0.0010 0.0003
## 300 1.1099 nan 0.0010 0.0002
## 320 1.0989 nan 0.0010 0.0002
## 340 1.0883 nan 0.0010 0.0002
## 360 1.0782 nan 0.0010 0.0002
## 380 1.0681 nan 0.0010 0.0002
## 400 1.0587 nan 0.0010 0.0002
## 420 1.0491 nan 0.0010 0.0002
## 440 1.0401 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0139 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0003
## 100 1.2400 nan 0.0010 0.0003
## 120 1.2253 nan 0.0010 0.0003
## 140 1.2111 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1835 nan 0.0010 0.0003
## 200 1.1706 nan 0.0010 0.0003
## 220 1.1581 nan 0.0010 0.0003
## 240 1.1460 nan 0.0010 0.0003
## 260 1.1341 nan 0.0010 0.0003
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1116 nan 0.0010 0.0003
## 320 1.1009 nan 0.0010 0.0003
## 340 1.0903 nan 0.0010 0.0002
## 360 1.0799 nan 0.0010 0.0002
## 380 1.0702 nan 0.0010 0.0002
## 400 1.0605 nan 0.0010 0.0002
## 420 1.0509 nan 0.0010 0.0002
## 440 1.0418 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0240 nan 0.0010 0.0002
## 500 1.0155 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2663 nan 0.0010 0.0003
## 80 1.2492 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0003
## 120 1.2173 nan 0.0010 0.0003
## 140 1.2022 nan 0.0010 0.0003
## 160 1.1875 nan 0.0010 0.0003
## 180 1.1732 nan 0.0010 0.0003
## 200 1.1594 nan 0.0010 0.0003
## 220 1.1456 nan 0.0010 0.0003
## 240 1.1328 nan 0.0010 0.0003
## 260 1.1203 nan 0.0010 0.0002
## 280 1.1084 nan 0.0010 0.0003
## 300 1.0965 nan 0.0010 0.0002
## 320 1.0850 nan 0.0010 0.0003
## 340 1.0737 nan 0.0010 0.0002
## 360 1.0628 nan 0.0010 0.0002
## 380 1.0521 nan 0.0010 0.0002
## 400 1.0420 nan 0.0010 0.0002
## 420 1.0320 nan 0.0010 0.0002
## 440 1.0221 nan 0.0010 0.0002
## 460 1.0129 nan 0.0010 0.0002
## 480 1.0038 nan 0.0010 0.0002
## 500 0.9946 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2182 nan 0.0010 0.0004
## 140 1.2032 nan 0.0010 0.0003
## 160 1.1885 nan 0.0010 0.0003
## 180 1.1744 nan 0.0010 0.0003
## 200 1.1607 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1218 nan 0.0010 0.0003
## 280 1.1094 nan 0.0010 0.0003
## 300 1.0976 nan 0.0010 0.0003
## 320 1.0860 nan 0.0010 0.0002
## 340 1.0750 nan 0.0010 0.0002
## 360 1.0642 nan 0.0010 0.0003
## 380 1.0539 nan 0.0010 0.0002
## 400 1.0440 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0152 nan 0.0010 0.0002
## 480 1.0061 nan 0.0010 0.0002
## 500 0.9972 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2357 nan 0.0010 0.0004
## 120 1.2201 nan 0.0010 0.0003
## 140 1.2050 nan 0.0010 0.0004
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1768 nan 0.0010 0.0003
## 200 1.1634 nan 0.0010 0.0003
## 220 1.1501 nan 0.0010 0.0003
## 240 1.1373 nan 0.0010 0.0003
## 260 1.1247 nan 0.0010 0.0002
## 280 1.1126 nan 0.0010 0.0002
## 300 1.1011 nan 0.0010 0.0003
## 320 1.0896 nan 0.0010 0.0003
## 340 1.0786 nan 0.0010 0.0002
## 360 1.0677 nan 0.0010 0.0003
## 380 1.0571 nan 0.0010 0.0002
## 400 1.0471 nan 0.0010 0.0002
## 420 1.0373 nan 0.0010 0.0002
## 440 1.0276 nan 0.0010 0.0002
## 460 1.0181 nan 0.0010 0.0002
## 480 1.0088 nan 0.0010 0.0002
## 500 0.9999 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0005
## 20 1.3011 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2638 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0004
## 100 1.2291 nan 0.0010 0.0003
## 120 1.2129 nan 0.0010 0.0004
## 140 1.1969 nan 0.0010 0.0004
## 160 1.1817 nan 0.0010 0.0003
## 180 1.1669 nan 0.0010 0.0003
## 200 1.1529 nan 0.0010 0.0003
## 220 1.1386 nan 0.0010 0.0003
## 240 1.1252 nan 0.0010 0.0003
## 260 1.1121 nan 0.0010 0.0003
## 280 1.0995 nan 0.0010 0.0002
## 300 1.0873 nan 0.0010 0.0003
## 320 1.0755 nan 0.0010 0.0003
## 340 1.0637 nan 0.0010 0.0003
## 360 1.0520 nan 0.0010 0.0002
## 380 1.0410 nan 0.0010 0.0002
## 400 1.0304 nan 0.0010 0.0002
## 420 1.0200 nan 0.0010 0.0002
## 440 1.0100 nan 0.0010 0.0002
## 460 1.0001 nan 0.0010 0.0002
## 480 0.9909 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3166 nan 0.0010 0.0005
## 5 1.3156 nan 0.0010 0.0005
## 6 1.3146 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3126 nan 0.0010 0.0004
## 9 1.3117 nan 0.0010 0.0005
## 10 1.3107 nan 0.0010 0.0004
## 20 1.3010 nan 0.0010 0.0004
## 40 1.2822 nan 0.0010 0.0004
## 60 1.2642 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0004
## 100 1.2302 nan 0.0010 0.0004
## 120 1.2142 nan 0.0010 0.0004
## 140 1.1982 nan 0.0010 0.0004
## 160 1.1828 nan 0.0010 0.0004
## 180 1.1680 nan 0.0010 0.0003
## 200 1.1540 nan 0.0010 0.0003
## 220 1.1403 nan 0.0010 0.0003
## 240 1.1269 nan 0.0010 0.0003
## 260 1.1140 nan 0.0010 0.0003
## 280 1.1015 nan 0.0010 0.0003
## 300 1.0890 nan 0.0010 0.0003
## 320 1.0770 nan 0.0010 0.0003
## 340 1.0653 nan 0.0010 0.0002
## 360 1.0542 nan 0.0010 0.0002
## 380 1.0435 nan 0.0010 0.0002
## 400 1.0327 nan 0.0010 0.0002
## 420 1.0223 nan 0.0010 0.0002
## 440 1.0123 nan 0.0010 0.0002
## 460 1.0027 nan 0.0010 0.0002
## 480 0.9935 nan 0.0010 0.0002
## 500 0.9843 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0005
## 20 1.3011 nan 0.0010 0.0005
## 40 1.2829 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0003
## 80 1.2480 nan 0.0010 0.0004
## 100 1.2314 nan 0.0010 0.0004
## 120 1.2153 nan 0.0010 0.0004
## 140 1.1995 nan 0.0010 0.0003
## 160 1.1846 nan 0.0010 0.0004
## 180 1.1699 nan 0.0010 0.0003
## 200 1.1558 nan 0.0010 0.0003
## 220 1.1426 nan 0.0010 0.0003
## 240 1.1294 nan 0.0010 0.0003
## 260 1.1167 nan 0.0010 0.0003
## 280 1.1041 nan 0.0010 0.0003
## 300 1.0917 nan 0.0010 0.0002
## 320 1.0800 nan 0.0010 0.0002
## 340 1.0686 nan 0.0010 0.0002
## 360 1.0574 nan 0.0010 0.0003
## 380 1.0467 nan 0.0010 0.0002
## 400 1.0361 nan 0.0010 0.0002
## 420 1.0262 nan 0.0010 0.0002
## 440 1.0165 nan 0.0010 0.0002
## 460 1.0070 nan 0.0010 0.0002
## 480 0.9976 nan 0.0010 0.0002
## 500 0.9885 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0043
## 3 1.2948 nan 0.0100 0.0035
## 4 1.2859 nan 0.0100 0.0042
## 5 1.2767 nan 0.0100 0.0040
## 6 1.2686 nan 0.0100 0.0040
## 7 1.2608 nan 0.0100 0.0038
## 8 1.2528 nan 0.0100 0.0038
## 9 1.2449 nan 0.0100 0.0035
## 10 1.2371 nan 0.0100 0.0036
## 20 1.1694 nan 0.0100 0.0024
## 40 1.0590 nan 0.0100 0.0021
## 60 0.9728 nan 0.0100 0.0016
## 80 0.9090 nan 0.0100 0.0014
## 100 0.8549 nan 0.0100 0.0009
## 120 0.8114 nan 0.0100 0.0006
## 140 0.7757 nan 0.0100 0.0006
## 160 0.7465 nan 0.0100 0.0004
## 180 0.7201 nan 0.0100 0.0004
## 200 0.6977 nan 0.0100 0.0003
## 220 0.6792 nan 0.0100 0.0002
## 240 0.6630 nan 0.0100 0.0001
## 260 0.6483 nan 0.0100 -0.0000
## 280 0.6337 nan 0.0100 -0.0001
## 300 0.6206 nan 0.0100 0.0001
## 320 0.6083 nan 0.0100 0.0002
## 340 0.5984 nan 0.0100 0.0001
## 360 0.5877 nan 0.0100 -0.0000
## 380 0.5763 nan 0.0100 0.0000
## 400 0.5671 nan 0.0100 -0.0000
## 420 0.5579 nan 0.0100 -0.0000
## 440 0.5495 nan 0.0100 -0.0001
## 460 0.5416 nan 0.0100 -0.0000
## 480 0.5335 nan 0.0100 -0.0001
## 500 0.5257 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0046
## 2 1.3017 nan 0.0100 0.0040
## 3 1.2934 nan 0.0100 0.0038
## 4 1.2854 nan 0.0100 0.0038
## 5 1.2779 nan 0.0100 0.0029
## 6 1.2694 nan 0.0100 0.0037
## 7 1.2625 nan 0.0100 0.0032
## 8 1.2542 nan 0.0100 0.0039
## 9 1.2470 nan 0.0100 0.0034
## 10 1.2391 nan 0.0100 0.0036
## 20 1.1702 nan 0.0100 0.0027
## 40 1.0574 nan 0.0100 0.0025
## 60 0.9739 nan 0.0100 0.0013
## 80 0.9094 nan 0.0100 0.0012
## 100 0.8563 nan 0.0100 0.0007
## 120 0.8140 nan 0.0100 0.0006
## 140 0.7782 nan 0.0100 0.0005
## 160 0.7490 nan 0.0100 0.0004
## 180 0.7233 nan 0.0100 0.0003
## 200 0.7024 nan 0.0100 0.0001
## 220 0.6819 nan 0.0100 0.0002
## 240 0.6654 nan 0.0100 0.0000
## 260 0.6505 nan 0.0100 -0.0000
## 280 0.6355 nan 0.0100 0.0001
## 300 0.6230 nan 0.0100 0.0001
## 320 0.6116 nan 0.0100 0.0000
## 340 0.6003 nan 0.0100 -0.0000
## 360 0.5906 nan 0.0100 0.0000
## 380 0.5809 nan 0.0100 0.0001
## 400 0.5708 nan 0.0100 0.0001
## 420 0.5618 nan 0.0100 -0.0001
## 440 0.5534 nan 0.0100 -0.0000
## 460 0.5449 nan 0.0100 -0.0001
## 480 0.5371 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0044
## 2 1.3027 nan 0.0100 0.0040
## 3 1.2947 nan 0.0100 0.0034
## 4 1.2861 nan 0.0100 0.0039
## 5 1.2781 nan 0.0100 0.0038
## 6 1.2703 nan 0.0100 0.0032
## 7 1.2627 nan 0.0100 0.0035
## 8 1.2543 nan 0.0100 0.0037
## 9 1.2474 nan 0.0100 0.0032
## 10 1.2399 nan 0.0100 0.0035
## 20 1.1713 nan 0.0100 0.0028
## 40 1.0600 nan 0.0100 0.0018
## 60 0.9744 nan 0.0100 0.0016
## 80 0.9093 nan 0.0100 0.0012
## 100 0.8570 nan 0.0100 0.0005
## 120 0.8142 nan 0.0100 0.0008
## 140 0.7795 nan 0.0100 0.0007
## 160 0.7500 nan 0.0100 0.0005
## 180 0.7254 nan 0.0100 0.0004
## 200 0.7038 nan 0.0100 0.0003
## 220 0.6857 nan 0.0100 0.0002
## 240 0.6684 nan 0.0100 0.0000
## 260 0.6541 nan 0.0100 0.0001
## 280 0.6412 nan 0.0100 -0.0000
## 300 0.6291 nan 0.0100 0.0000
## 320 0.6175 nan 0.0100 -0.0000
## 340 0.6070 nan 0.0100 0.0000
## 360 0.5960 nan 0.0100 0.0000
## 380 0.5871 nan 0.0100 0.0001
## 400 0.5782 nan 0.0100 0.0000
## 420 0.5692 nan 0.0100 -0.0002
## 440 0.5604 nan 0.0100 -0.0001
## 460 0.5526 nan 0.0100 -0.0001
## 480 0.5446 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0043
## 2 1.3015 nan 0.0100 0.0046
## 3 1.2926 nan 0.0100 0.0040
## 4 1.2839 nan 0.0100 0.0040
## 5 1.2755 nan 0.0100 0.0036
## 6 1.2665 nan 0.0100 0.0042
## 7 1.2580 nan 0.0100 0.0038
## 8 1.2493 nan 0.0100 0.0039
## 9 1.2408 nan 0.0100 0.0038
## 10 1.2337 nan 0.0100 0.0032
## 20 1.1601 nan 0.0100 0.0031
## 40 1.0446 nan 0.0100 0.0023
## 60 0.9559 nan 0.0100 0.0017
## 80 0.8868 nan 0.0100 0.0012
## 100 0.8304 nan 0.0100 0.0010
## 120 0.7861 nan 0.0100 0.0009
## 140 0.7481 nan 0.0100 0.0005
## 160 0.7169 nan 0.0100 0.0005
## 180 0.6900 nan 0.0100 0.0003
## 200 0.6667 nan 0.0100 0.0004
## 220 0.6460 nan 0.0100 0.0001
## 240 0.6267 nan 0.0100 0.0001
## 260 0.6095 nan 0.0100 0.0001
## 280 0.5940 nan 0.0100 0.0000
## 300 0.5795 nan 0.0100 0.0001
## 320 0.5667 nan 0.0100 0.0001
## 340 0.5545 nan 0.0100 -0.0002
## 360 0.5424 nan 0.0100 -0.0001
## 380 0.5318 nan 0.0100 -0.0001
## 400 0.5210 nan 0.0100 0.0001
## 420 0.5109 nan 0.0100 -0.0000
## 440 0.5018 nan 0.0100 0.0001
## 460 0.4930 nan 0.0100 0.0001
## 480 0.4835 nan 0.0100 -0.0000
## 500 0.4757 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0046
## 2 1.3017 nan 0.0100 0.0039
## 3 1.2933 nan 0.0100 0.0036
## 4 1.2842 nan 0.0100 0.0039
## 5 1.2756 nan 0.0100 0.0042
## 6 1.2680 nan 0.0100 0.0035
## 7 1.2588 nan 0.0100 0.0037
## 8 1.2507 nan 0.0100 0.0033
## 9 1.2419 nan 0.0100 0.0037
## 10 1.2338 nan 0.0100 0.0041
## 20 1.1604 nan 0.0100 0.0029
## 40 1.0453 nan 0.0100 0.0021
## 60 0.9556 nan 0.0100 0.0015
## 80 0.8870 nan 0.0100 0.0011
## 100 0.8312 nan 0.0100 0.0008
## 120 0.7864 nan 0.0100 0.0008
## 140 0.7494 nan 0.0100 0.0004
## 160 0.7178 nan 0.0100 0.0002
## 180 0.6922 nan 0.0100 0.0003
## 200 0.6694 nan 0.0100 0.0002
## 220 0.6498 nan 0.0100 -0.0000
## 240 0.6315 nan 0.0100 0.0002
## 260 0.6152 nan 0.0100 0.0002
## 280 0.6005 nan 0.0100 0.0001
## 300 0.5870 nan 0.0100 0.0000
## 320 0.5732 nan 0.0100 -0.0000
## 340 0.5609 nan 0.0100 -0.0000
## 360 0.5495 nan 0.0100 -0.0001
## 380 0.5387 nan 0.0100 -0.0001
## 400 0.5287 nan 0.0100 -0.0001
## 420 0.5180 nan 0.0100 0.0000
## 440 0.5084 nan 0.0100 0.0001
## 460 0.4989 nan 0.0100 -0.0001
## 480 0.4901 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0047
## 2 1.3012 nan 0.0100 0.0049
## 3 1.2917 nan 0.0100 0.0047
## 4 1.2831 nan 0.0100 0.0039
## 5 1.2736 nan 0.0100 0.0045
## 6 1.2655 nan 0.0100 0.0038
## 7 1.2574 nan 0.0100 0.0040
## 8 1.2493 nan 0.0100 0.0036
## 9 1.2407 nan 0.0100 0.0035
## 10 1.2330 nan 0.0100 0.0032
## 20 1.1589 nan 0.0100 0.0031
## 40 1.0464 nan 0.0100 0.0025
## 60 0.9586 nan 0.0100 0.0016
## 80 0.8905 nan 0.0100 0.0013
## 100 0.8357 nan 0.0100 0.0007
## 120 0.7913 nan 0.0100 0.0006
## 140 0.7533 nan 0.0100 0.0006
## 160 0.7235 nan 0.0100 0.0006
## 180 0.6978 nan 0.0100 0.0001
## 200 0.6742 nan 0.0100 0.0001
## 220 0.6544 nan 0.0100 0.0002
## 240 0.6369 nan 0.0100 0.0002
## 260 0.6202 nan 0.0100 0.0001
## 280 0.6060 nan 0.0100 0.0002
## 300 0.5914 nan 0.0100 0.0000
## 320 0.5781 nan 0.0100 -0.0001
## 340 0.5668 nan 0.0100 -0.0000
## 360 0.5556 nan 0.0100 -0.0001
## 380 0.5443 nan 0.0100 -0.0001
## 400 0.5351 nan 0.0100 -0.0000
## 420 0.5251 nan 0.0100 -0.0001
## 440 0.5153 nan 0.0100 0.0000
## 460 0.5060 nan 0.0100 -0.0002
## 480 0.4974 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0044
## 2 1.3017 nan 0.0100 0.0039
## 3 1.2927 nan 0.0100 0.0037
## 4 1.2836 nan 0.0100 0.0045
## 5 1.2739 nan 0.0100 0.0043
## 6 1.2650 nan 0.0100 0.0041
## 7 1.2566 nan 0.0100 0.0040
## 8 1.2474 nan 0.0100 0.0040
## 9 1.2387 nan 0.0100 0.0042
## 10 1.2297 nan 0.0100 0.0043
## 20 1.1510 nan 0.0100 0.0032
## 40 1.0302 nan 0.0100 0.0019
## 60 0.9386 nan 0.0100 0.0016
## 80 0.8680 nan 0.0100 0.0011
## 100 0.8104 nan 0.0100 0.0011
## 120 0.7640 nan 0.0100 0.0008
## 140 0.7255 nan 0.0100 0.0007
## 160 0.6915 nan 0.0100 0.0003
## 180 0.6626 nan 0.0100 0.0005
## 200 0.6373 nan 0.0100 0.0003
## 220 0.6144 nan 0.0100 0.0000
## 240 0.5954 nan 0.0100 0.0000
## 260 0.5768 nan 0.0100 0.0001
## 280 0.5601 nan 0.0100 0.0000
## 300 0.5455 nan 0.0100 0.0001
## 320 0.5313 nan 0.0100 0.0000
## 340 0.5171 nan 0.0100 0.0001
## 360 0.5049 nan 0.0100 0.0002
## 380 0.4926 nan 0.0100 -0.0001
## 400 0.4809 nan 0.0100 0.0000
## 420 0.4695 nan 0.0100 0.0001
## 440 0.4592 nan 0.0100 -0.0001
## 460 0.4496 nan 0.0100 -0.0002
## 480 0.4402 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3013 nan 0.0100 0.0045
## 3 1.2921 nan 0.0100 0.0037
## 4 1.2831 nan 0.0100 0.0043
## 5 1.2740 nan 0.0100 0.0042
## 6 1.2652 nan 0.0100 0.0042
## 7 1.2565 nan 0.0100 0.0040
## 8 1.2474 nan 0.0100 0.0041
## 9 1.2391 nan 0.0100 0.0039
## 10 1.2311 nan 0.0100 0.0035
## 20 1.1535 nan 0.0100 0.0033
## 40 1.0334 nan 0.0100 0.0024
## 60 0.9422 nan 0.0100 0.0020
## 80 0.8699 nan 0.0100 0.0011
## 100 0.8136 nan 0.0100 0.0007
## 120 0.7653 nan 0.0100 0.0004
## 140 0.7273 nan 0.0100 0.0006
## 160 0.6955 nan 0.0100 0.0004
## 180 0.6672 nan 0.0100 0.0004
## 200 0.6414 nan 0.0100 0.0002
## 220 0.6202 nan 0.0100 0.0002
## 240 0.6009 nan 0.0100 0.0003
## 260 0.5825 nan 0.0100 -0.0000
## 280 0.5664 nan 0.0100 0.0001
## 300 0.5521 nan 0.0100 -0.0000
## 320 0.5383 nan 0.0100 0.0001
## 340 0.5252 nan 0.0100 0.0000
## 360 0.5128 nan 0.0100 0.0001
## 380 0.5014 nan 0.0100 -0.0001
## 400 0.4894 nan 0.0100 -0.0000
## 420 0.4786 nan 0.0100 0.0000
## 440 0.4678 nan 0.0100 0.0001
## 460 0.4574 nan 0.0100 -0.0001
## 480 0.4479 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3015 nan 0.0100 0.0045
## 3 1.2916 nan 0.0100 0.0045
## 4 1.2815 nan 0.0100 0.0044
## 5 1.2724 nan 0.0100 0.0038
## 6 1.2634 nan 0.0100 0.0037
## 7 1.2552 nan 0.0100 0.0039
## 8 1.2466 nan 0.0100 0.0040
## 9 1.2385 nan 0.0100 0.0037
## 10 1.2303 nan 0.0100 0.0039
## 20 1.1551 nan 0.0100 0.0034
## 40 1.0345 nan 0.0100 0.0024
## 60 0.9460 nan 0.0100 0.0018
## 80 0.8750 nan 0.0100 0.0013
## 100 0.8180 nan 0.0100 0.0012
## 120 0.7737 nan 0.0100 0.0007
## 140 0.7361 nan 0.0100 0.0003
## 160 0.7041 nan 0.0100 0.0004
## 180 0.6763 nan 0.0100 0.0003
## 200 0.6520 nan 0.0100 0.0001
## 220 0.6301 nan 0.0100 -0.0000
## 240 0.6115 nan 0.0100 0.0002
## 260 0.5947 nan 0.0100 0.0001
## 280 0.5786 nan 0.0100 0.0002
## 300 0.5634 nan 0.0100 0.0000
## 320 0.5501 nan 0.0100 -0.0001
## 340 0.5362 nan 0.0100 -0.0002
## 360 0.5239 nan 0.0100 0.0000
## 380 0.5113 nan 0.0100 0.0001
## 400 0.5002 nan 0.0100 -0.0001
## 420 0.4894 nan 0.0100 -0.0000
## 440 0.4796 nan 0.0100 -0.0002
## 460 0.4697 nan 0.0100 0.0002
## 480 0.4598 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0405
## 2 1.1692 nan 0.1000 0.0304
## 3 1.1095 nan 0.1000 0.0311
## 4 1.0607 nan 0.1000 0.0175
## 5 1.0082 nan 0.1000 0.0223
## 6 0.9687 nan 0.1000 0.0167
## 7 0.9350 nan 0.1000 0.0138
## 8 0.9013 nan 0.1000 0.0127
## 9 0.8744 nan 0.1000 0.0105
## 10 0.8507 nan 0.1000 0.0086
## 20 0.7061 nan 0.1000 0.0030
## 40 0.5755 nan 0.1000 -0.0006
## 60 0.4942 nan 0.1000 -0.0003
## 80 0.4354 nan 0.1000 0.0004
## 100 0.3833 nan 0.1000 -0.0005
## 120 0.3425 nan 0.1000 -0.0017
## 140 0.3034 nan 0.1000 -0.0007
## 160 0.2735 nan 0.1000 -0.0001
## 180 0.2482 nan 0.1000 -0.0006
## 200 0.2198 nan 0.1000 -0.0010
## 220 0.2005 nan 0.1000 -0.0002
## 240 0.1834 nan 0.1000 -0.0003
## 260 0.1671 nan 0.1000 -0.0004
## 280 0.1527 nan 0.1000 -0.0004
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## 320 0.1284 nan 0.1000 -0.0003
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## 360 0.1092 nan 0.1000 -0.0001
## 380 0.1000 nan 0.1000 -0.0001
## 400 0.0927 nan 0.1000 -0.0002
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## 480 0.0682 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0389
## 2 1.1719 nan 0.1000 0.0227
## 3 1.1175 nan 0.1000 0.0235
## 4 1.0626 nan 0.1000 0.0244
## 5 1.0158 nan 0.1000 0.0202
## 6 0.9778 nan 0.1000 0.0158
## 7 0.9431 nan 0.1000 0.0131
## 8 0.9037 nan 0.1000 0.0158
## 9 0.8779 nan 0.1000 0.0104
## 10 0.8516 nan 0.1000 0.0097
## 20 0.7113 nan 0.1000 -0.0004
## 40 0.5795 nan 0.1000 0.0010
## 60 0.5005 nan 0.1000 -0.0006
## 80 0.4415 nan 0.1000 -0.0021
## 100 0.3931 nan 0.1000 -0.0020
## 120 0.3505 nan 0.1000 -0.0005
## 140 0.3131 nan 0.1000 0.0003
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## 180 0.2525 nan 0.1000 -0.0005
## 200 0.2289 nan 0.1000 -0.0007
## 220 0.2082 nan 0.1000 -0.0007
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## 300 0.1436 nan 0.1000 -0.0007
## 320 0.1326 nan 0.1000 -0.0004
## 340 0.1219 nan 0.1000 -0.0003
## 360 0.1116 nan 0.1000 -0.0002
## 380 0.1028 nan 0.1000 -0.0000
## 400 0.0958 nan 0.1000 -0.0003
## 420 0.0881 nan 0.1000 -0.0003
## 440 0.0811 nan 0.1000 -0.0004
## 460 0.0753 nan 0.1000 -0.0001
## 480 0.0697 nan 0.1000 -0.0000
## 500 0.0646 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2321 nan 0.1000 0.0392
## 2 1.1564 nan 0.1000 0.0367
## 3 1.0986 nan 0.1000 0.0277
## 4 1.0515 nan 0.1000 0.0206
## 5 1.0080 nan 0.1000 0.0194
## 6 0.9677 nan 0.1000 0.0188
## 7 0.9358 nan 0.1000 0.0124
## 8 0.9005 nan 0.1000 0.0142
## 9 0.8730 nan 0.1000 0.0105
## 10 0.8466 nan 0.1000 0.0114
## 20 0.6934 nan 0.1000 0.0023
## 40 0.5712 nan 0.1000 -0.0003
## 60 0.5006 nan 0.1000 -0.0016
## 80 0.4457 nan 0.1000 -0.0019
## 100 0.3930 nan 0.1000 -0.0009
## 120 0.3486 nan 0.1000 -0.0014
## 140 0.3184 nan 0.1000 -0.0011
## 160 0.2861 nan 0.1000 -0.0008
## 180 0.2644 nan 0.1000 -0.0009
## 200 0.2395 nan 0.1000 -0.0009
## 220 0.2199 nan 0.1000 -0.0018
## 240 0.2010 nan 0.1000 -0.0002
## 260 0.1854 nan 0.1000 -0.0009
## 280 0.1703 nan 0.1000 -0.0010
## 300 0.1575 nan 0.1000 -0.0006
## 320 0.1449 nan 0.1000 -0.0009
## 340 0.1314 nan 0.1000 -0.0002
## 360 0.1212 nan 0.1000 -0.0003
## 380 0.1121 nan 0.1000 -0.0007
## 400 0.1043 nan 0.1000 -0.0006
## 420 0.0970 nan 0.1000 -0.0001
## 440 0.0899 nan 0.1000 -0.0002
## 460 0.0830 nan 0.1000 -0.0004
## 480 0.0774 nan 0.1000 -0.0003
## 500 0.0710 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0393
## 2 1.1512 nan 0.1000 0.0391
## 3 1.0878 nan 0.1000 0.0272
## 4 1.0339 nan 0.1000 0.0247
## 5 0.9880 nan 0.1000 0.0185
## 6 0.9479 nan 0.1000 0.0148
## 7 0.9043 nan 0.1000 0.0184
## 8 0.8661 nan 0.1000 0.0149
## 9 0.8369 nan 0.1000 0.0110
## 10 0.8136 nan 0.1000 0.0088
## 20 0.6606 nan 0.1000 0.0040
## 40 0.5210 nan 0.1000 -0.0003
## 60 0.4408 nan 0.1000 -0.0002
## 80 0.3806 nan 0.1000 -0.0004
## 100 0.3337 nan 0.1000 -0.0017
## 120 0.2903 nan 0.1000 -0.0002
## 140 0.2539 nan 0.1000 -0.0007
## 160 0.2208 nan 0.1000 -0.0008
## 180 0.1963 nan 0.1000 -0.0004
## 200 0.1752 nan 0.1000 -0.0010
## 220 0.1555 nan 0.1000 -0.0006
## 240 0.1380 nan 0.1000 -0.0002
## 260 0.1238 nan 0.1000 -0.0001
## 280 0.1087 nan 0.1000 -0.0003
## 300 0.0979 nan 0.1000 -0.0005
## 320 0.0887 nan 0.1000 -0.0003
## 340 0.0800 nan 0.1000 -0.0002
## 360 0.0717 nan 0.1000 -0.0000
## 380 0.0642 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 0.0000
## 420 0.0526 nan 0.1000 -0.0001
## 440 0.0472 nan 0.1000 -0.0001
## 460 0.0432 nan 0.1000 -0.0003
## 480 0.0392 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2282 nan 0.1000 0.0431
## 2 1.1537 nan 0.1000 0.0291
## 3 1.0933 nan 0.1000 0.0303
## 4 1.0353 nan 0.1000 0.0240
## 5 0.9882 nan 0.1000 0.0202
## 6 0.9473 nan 0.1000 0.0159
## 7 0.9139 nan 0.1000 0.0142
## 8 0.8803 nan 0.1000 0.0154
## 9 0.8508 nan 0.1000 0.0108
## 10 0.8249 nan 0.1000 0.0092
## 20 0.6708 nan 0.1000 0.0035
## 40 0.5247 nan 0.1000 -0.0006
## 60 0.4458 nan 0.1000 -0.0003
## 80 0.3874 nan 0.1000 -0.0009
## 100 0.3330 nan 0.1000 -0.0010
## 120 0.2922 nan 0.1000 -0.0009
## 140 0.2581 nan 0.1000 -0.0005
## 160 0.2277 nan 0.1000 -0.0010
## 180 0.2007 nan 0.1000 -0.0007
## 200 0.1746 nan 0.1000 -0.0002
## 220 0.1557 nan 0.1000 -0.0007
## 240 0.1387 nan 0.1000 -0.0005
## 260 0.1238 nan 0.1000 -0.0007
## 280 0.1095 nan 0.1000 -0.0003
## 300 0.0991 nan 0.1000 -0.0006
## 320 0.0884 nan 0.1000 -0.0002
## 340 0.0800 nan 0.1000 -0.0003
## 360 0.0725 nan 0.1000 -0.0002
## 380 0.0654 nan 0.1000 -0.0002
## 400 0.0591 nan 0.1000 -0.0001
## 420 0.0539 nan 0.1000 -0.0001
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## 460 0.0440 nan 0.1000 -0.0002
## 480 0.0405 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2327 nan 0.1000 0.0386
## 2 1.1570 nan 0.1000 0.0321
## 3 1.0957 nan 0.1000 0.0276
## 4 1.0404 nan 0.1000 0.0227
## 5 0.9950 nan 0.1000 0.0198
## 6 0.9520 nan 0.1000 0.0199
## 7 0.9171 nan 0.1000 0.0135
## 8 0.8858 nan 0.1000 0.0131
## 9 0.8586 nan 0.1000 0.0106
## 10 0.8344 nan 0.1000 0.0094
## 20 0.6821 nan 0.1000 0.0020
## 40 0.5429 nan 0.1000 -0.0007
## 60 0.4664 nan 0.1000 0.0003
## 80 0.3961 nan 0.1000 -0.0016
## 100 0.3438 nan 0.1000 -0.0008
## 120 0.3024 nan 0.1000 -0.0005
## 140 0.2675 nan 0.1000 -0.0007
## 160 0.2390 nan 0.1000 -0.0010
## 180 0.2135 nan 0.1000 -0.0006
## 200 0.1894 nan 0.1000 -0.0008
## 220 0.1719 nan 0.1000 -0.0010
## 240 0.1527 nan 0.1000 -0.0008
## 260 0.1374 nan 0.1000 -0.0008
## 280 0.1231 nan 0.1000 -0.0004
## 300 0.1117 nan 0.1000 -0.0002
## 320 0.0993 nan 0.1000 -0.0001
## 340 0.0904 nan 0.1000 -0.0003
## 360 0.0817 nan 0.1000 -0.0003
## 380 0.0749 nan 0.1000 -0.0003
## 400 0.0676 nan 0.1000 -0.0001
## 420 0.0616 nan 0.1000 -0.0003
## 440 0.0565 nan 0.1000 -0.0002
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## 480 0.0467 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0346
## 2 1.1571 nan 0.1000 0.0344
## 3 1.0977 nan 0.1000 0.0257
## 4 1.0409 nan 0.1000 0.0270
## 5 0.9903 nan 0.1000 0.0203
## 6 0.9452 nan 0.1000 0.0175
## 7 0.9018 nan 0.1000 0.0158
## 8 0.8686 nan 0.1000 0.0123
## 9 0.8355 nan 0.1000 0.0128
## 10 0.8071 nan 0.1000 0.0108
## 20 0.6300 nan 0.1000 0.0037
## 40 0.4813 nan 0.1000 -0.0010
## 60 0.3973 nan 0.1000 -0.0007
## 80 0.3374 nan 0.1000 -0.0013
## 100 0.2818 nan 0.1000 -0.0005
## 120 0.2374 nan 0.1000 -0.0002
## 140 0.1971 nan 0.1000 -0.0005
## 160 0.1686 nan 0.1000 0.0000
## 180 0.1428 nan 0.1000 -0.0006
## 200 0.1242 nan 0.1000 -0.0003
## 220 0.1089 nan 0.1000 -0.0002
## 240 0.0947 nan 0.1000 -0.0002
## 260 0.0838 nan 0.1000 -0.0002
## 280 0.0731 nan 0.1000 -0.0003
## 300 0.0650 nan 0.1000 -0.0002
## 320 0.0571 nan 0.1000 0.0000
## 340 0.0503 nan 0.1000 -0.0002
## 360 0.0450 nan 0.1000 -0.0001
## 380 0.0403 nan 0.1000 -0.0001
## 400 0.0362 nan 0.1000 -0.0001
## 420 0.0322 nan 0.1000 -0.0001
## 440 0.0288 nan 0.1000 -0.0002
## 460 0.0254 nan 0.1000 -0.0001
## 480 0.0227 nan 0.1000 -0.0001
## 500 0.0205 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2295 nan 0.1000 0.0449
## 2 1.1563 nan 0.1000 0.0319
## 3 1.0953 nan 0.1000 0.0245
## 4 1.0412 nan 0.1000 0.0236
## 5 0.9897 nan 0.1000 0.0210
## 6 0.9450 nan 0.1000 0.0196
## 7 0.9100 nan 0.1000 0.0147
## 8 0.8732 nan 0.1000 0.0138
## 9 0.8414 nan 0.1000 0.0129
## 10 0.8153 nan 0.1000 0.0096
## 20 0.6412 nan 0.1000 0.0020
## 40 0.4965 nan 0.1000 -0.0014
## 60 0.4056 nan 0.1000 -0.0001
## 80 0.3344 nan 0.1000 -0.0013
## 100 0.2833 nan 0.1000 -0.0001
## 120 0.2386 nan 0.1000 -0.0010
## 140 0.2043 nan 0.1000 -0.0011
## 160 0.1778 nan 0.1000 -0.0007
## 180 0.1549 nan 0.1000 -0.0007
## 200 0.1336 nan 0.1000 -0.0001
## 220 0.1186 nan 0.1000 -0.0005
## 240 0.1041 nan 0.1000 -0.0004
## 260 0.0907 nan 0.1000 -0.0003
## 280 0.0805 nan 0.1000 -0.0001
## 300 0.0713 nan 0.1000 -0.0002
## 320 0.0623 nan 0.1000 -0.0001
## 340 0.0557 nan 0.1000 -0.0001
## 360 0.0492 nan 0.1000 -0.0002
## 380 0.0444 nan 0.1000 -0.0001
## 400 0.0391 nan 0.1000 -0.0001
## 420 0.0349 nan 0.1000 -0.0002
## 440 0.0309 nan 0.1000 -0.0001
## 460 0.0272 nan 0.1000 -0.0001
## 480 0.0240 nan 0.1000 -0.0001
## 500 0.0213 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0380
## 2 1.1560 nan 0.1000 0.0391
## 3 1.0902 nan 0.1000 0.0264
## 4 1.0351 nan 0.1000 0.0220
## 5 0.9853 nan 0.1000 0.0200
## 6 0.9378 nan 0.1000 0.0182
## 7 0.9012 nan 0.1000 0.0139
## 8 0.8689 nan 0.1000 0.0116
## 9 0.8385 nan 0.1000 0.0118
## 10 0.8131 nan 0.1000 0.0103
## 20 0.6441 nan 0.1000 0.0029
## 40 0.5068 nan 0.1000 -0.0000
## 60 0.4147 nan 0.1000 -0.0011
## 80 0.3472 nan 0.1000 -0.0018
## 100 0.2881 nan 0.1000 -0.0006
## 120 0.2494 nan 0.1000 -0.0002
## 140 0.2124 nan 0.1000 -0.0002
## 160 0.1819 nan 0.1000 -0.0002
## 180 0.1594 nan 0.1000 -0.0003
## 200 0.1390 nan 0.1000 -0.0006
## 220 0.1212 nan 0.1000 -0.0008
## 240 0.1074 nan 0.1000 -0.0006
## 260 0.0946 nan 0.1000 -0.0003
## 280 0.0838 nan 0.1000 -0.0002
## 300 0.0740 nan 0.1000 0.0001
## 320 0.0650 nan 0.1000 -0.0002
## 340 0.0575 nan 0.1000 -0.0002
## 360 0.0512 nan 0.1000 -0.0002
## 380 0.0458 nan 0.1000 -0.0002
## 400 0.0405 nan 0.1000 -0.0001
## 420 0.0357 nan 0.1000 -0.0001
## 440 0.0322 nan 0.1000 -0.0002
## 460 0.0290 nan 0.1000 -0.0002
## 480 0.0259 nan 0.1000 -0.0002
## 500 0.0233 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3205 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3172 nan 0.0010 0.0003
## 6 1.3164 nan 0.0010 0.0004
## 7 1.3156 nan 0.0010 0.0004
## 8 1.3148 nan 0.0010 0.0004
## 9 1.3140 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2309 nan 0.0010 0.0003
## 140 1.2174 nan 0.0010 0.0003
## 160 1.2041 nan 0.0010 0.0003
## 180 1.1916 nan 0.0010 0.0002
## 200 1.1797 nan 0.0010 0.0002
## 220 1.1681 nan 0.0010 0.0003
## 240 1.1567 nan 0.0010 0.0003
## 260 1.1457 nan 0.0010 0.0002
## 280 1.1349 nan 0.0010 0.0002
## 300 1.1245 nan 0.0010 0.0002
## 320 1.1141 nan 0.0010 0.0002
## 340 1.1044 nan 0.0010 0.0002
## 360 1.0946 nan 0.0010 0.0002
## 380 1.0852 nan 0.0010 0.0002
## 400 1.0759 nan 0.0010 0.0002
## 420 1.0671 nan 0.0010 0.0002
## 440 1.0588 nan 0.0010 0.0001
## 460 1.0504 nan 0.0010 0.0002
## 480 1.0421 nan 0.0010 0.0002
## 500 1.0340 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0003
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0004
## 40 1.2890 nan 0.0010 0.0004
## 60 1.2736 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0004
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2310 nan 0.0010 0.0003
## 140 1.2176 nan 0.0010 0.0003
## 160 1.2046 nan 0.0010 0.0003
## 180 1.1920 nan 0.0010 0.0003
## 200 1.1797 nan 0.0010 0.0003
## 220 1.1680 nan 0.0010 0.0003
## 240 1.1568 nan 0.0010 0.0002
## 260 1.1457 nan 0.0010 0.0002
## 280 1.1348 nan 0.0010 0.0002
## 300 1.1243 nan 0.0010 0.0002
## 320 1.1142 nan 0.0010 0.0002
## 340 1.1042 nan 0.0010 0.0002
## 360 1.0947 nan 0.0010 0.0002
## 380 1.0852 nan 0.0010 0.0002
## 400 1.0760 nan 0.0010 0.0002
## 420 1.0673 nan 0.0010 0.0002
## 440 1.0586 nan 0.0010 0.0002
## 460 1.0505 nan 0.0010 0.0002
## 480 1.0425 nan 0.0010 0.0002
## 500 1.0346 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3180 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3164 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0004
## 9 1.3139 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2897 nan 0.0010 0.0003
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2601 nan 0.0010 0.0003
## 100 1.2457 nan 0.0010 0.0003
## 120 1.2319 nan 0.0010 0.0003
## 140 1.2189 nan 0.0010 0.0003
## 160 1.2060 nan 0.0010 0.0003
## 180 1.1935 nan 0.0010 0.0003
## 200 1.1814 nan 0.0010 0.0002
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1582 nan 0.0010 0.0003
## 260 1.1473 nan 0.0010 0.0002
## 280 1.1364 nan 0.0010 0.0002
## 300 1.1257 nan 0.0010 0.0003
## 320 1.1157 nan 0.0010 0.0002
## 340 1.1058 nan 0.0010 0.0002
## 360 1.0961 nan 0.0010 0.0002
## 380 1.0866 nan 0.0010 0.0002
## 400 1.0777 nan 0.0010 0.0002
## 420 1.0687 nan 0.0010 0.0002
## 440 1.0599 nan 0.0010 0.0001
## 460 1.0514 nan 0.0010 0.0002
## 480 1.0434 nan 0.0010 0.0002
## 500 1.0353 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0003
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0003
## 80 1.2550 nan 0.0010 0.0003
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1967 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1705 nan 0.0010 0.0003
## 220 1.1581 nan 0.0010 0.0003
## 240 1.1458 nan 0.0010 0.0003
## 260 1.1340 nan 0.0010 0.0002
## 280 1.1224 nan 0.0010 0.0002
## 300 1.1112 nan 0.0010 0.0002
## 320 1.1002 nan 0.0010 0.0002
## 340 1.0895 nan 0.0010 0.0002
## 360 1.0796 nan 0.0010 0.0002
## 380 1.0695 nan 0.0010 0.0002
## 400 1.0596 nan 0.0010 0.0002
## 420 1.0501 nan 0.0010 0.0002
## 440 1.0411 nan 0.0010 0.0002
## 460 1.0319 nan 0.0010 0.0002
## 480 1.0233 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0003
## 60 1.2711 nan 0.0010 0.0003
## 80 1.2557 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0004
## 120 1.2258 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1983 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1594 nan 0.0010 0.0003
## 240 1.1473 nan 0.0010 0.0002
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## 280 1.1241 nan 0.0010 0.0003
## 300 1.1130 nan 0.0010 0.0002
## 320 1.1021 nan 0.0010 0.0002
## 340 1.0914 nan 0.0010 0.0002
## 360 1.0810 nan 0.0010 0.0002
## 380 1.0707 nan 0.0010 0.0002
## 400 1.0611 nan 0.0010 0.0002
## 420 1.0517 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0003
## 60 1.2709 nan 0.0010 0.0004
## 80 1.2553 nan 0.0010 0.0003
## 100 1.2400 nan 0.0010 0.0004
## 120 1.2256 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
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## 180 1.1847 nan 0.0010 0.0003
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## 400 1.0624 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
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## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2678 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
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## 20 1.3031 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0003
## 80 1.2520 nan 0.0010 0.0003
## 100 1.2358 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1643 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1388 nan 0.0010 0.0003
## 260 1.1266 nan 0.0010 0.0002
## 280 1.1146 nan 0.0010 0.0003
## 300 1.1029 nan 0.0010 0.0002
## 320 1.0919 nan 0.0010 0.0002
## 340 1.0810 nan 0.0010 0.0002
## 360 1.0707 nan 0.0010 0.0002
## 380 1.0605 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
## 420 1.0412 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0223 nan 0.0010 0.0002
## 480 1.0132 nan 0.0010 0.0002
## 500 1.0045 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0003
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0004
## 100 1.2369 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1935 nan 0.0010 0.0002
## 180 1.1798 nan 0.0010 0.0003
## 200 1.1665 nan 0.0010 0.0003
## 220 1.1538 nan 0.0010 0.0002
## 240 1.1413 nan 0.0010 0.0003
## 260 1.1292 nan 0.0010 0.0002
## 280 1.1177 nan 0.0010 0.0002
## 300 1.1059 nan 0.0010 0.0003
## 320 1.0947 nan 0.0010 0.0002
## 340 1.0840 nan 0.0010 0.0002
## 360 1.0736 nan 0.0010 0.0003
## 380 1.0633 nan 0.0010 0.0002
## 400 1.0532 nan 0.0010 0.0002
## 420 1.0434 nan 0.0010 0.0002
## 440 1.0337 nan 0.0010 0.0002
## 460 1.0245 nan 0.0010 0.0002
## 480 1.0157 nan 0.0010 0.0001
## 500 1.0070 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0039
## 3 1.2959 nan 0.0100 0.0034
## 4 1.2880 nan 0.0100 0.0038
## 5 1.2807 nan 0.0100 0.0031
## 6 1.2726 nan 0.0100 0.0038
## 7 1.2652 nan 0.0100 0.0033
## 8 1.2574 nan 0.0100 0.0035
## 9 1.2504 nan 0.0100 0.0031
## 10 1.2435 nan 0.0100 0.0030
## 20 1.1791 nan 0.0100 0.0029
## 40 1.0735 nan 0.0100 0.0020
## 60 0.9945 nan 0.0100 0.0016
## 80 0.9333 nan 0.0100 0.0012
## 100 0.8842 nan 0.0100 0.0008
## 120 0.8432 nan 0.0100 0.0006
## 140 0.8089 nan 0.0100 0.0005
## 160 0.7786 nan 0.0100 0.0003
## 180 0.7524 nan 0.0100 0.0003
## 200 0.7298 nan 0.0100 0.0003
## 220 0.7097 nan 0.0100 0.0003
## 240 0.6914 nan 0.0100 0.0003
## 260 0.6757 nan 0.0100 -0.0001
## 280 0.6616 nan 0.0100 0.0000
## 300 0.6490 nan 0.0100 0.0000
## 320 0.6367 nan 0.0100 0.0001
## 340 0.6247 nan 0.0100 0.0000
## 360 0.6136 nan 0.0100 -0.0001
## 380 0.6030 nan 0.0100 -0.0000
## 400 0.5928 nan 0.0100 -0.0002
## 420 0.5825 nan 0.0100 -0.0002
## 440 0.5737 nan 0.0100 0.0001
## 460 0.5642 nan 0.0100 0.0000
## 480 0.5551 nan 0.0100 0.0000
## 500 0.5465 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0036
## 2 1.3057 nan 0.0100 0.0033
## 3 1.2979 nan 0.0100 0.0035
## 4 1.2906 nan 0.0100 0.0030
## 5 1.2839 nan 0.0100 0.0033
## 6 1.2764 nan 0.0100 0.0035
## 7 1.2697 nan 0.0100 0.0028
## 8 1.2619 nan 0.0100 0.0038
## 9 1.2545 nan 0.0100 0.0035
## 10 1.2471 nan 0.0100 0.0035
## 20 1.1810 nan 0.0100 0.0028
## 40 1.0764 nan 0.0100 0.0020
## 60 0.9969 nan 0.0100 0.0015
## 80 0.9351 nan 0.0100 0.0012
## 100 0.8833 nan 0.0100 0.0008
## 120 0.8428 nan 0.0100 0.0005
## 140 0.8092 nan 0.0100 0.0005
## 160 0.7803 nan 0.0100 0.0006
## 180 0.7541 nan 0.0100 0.0002
## 200 0.7322 nan 0.0100 0.0003
## 220 0.7126 nan 0.0100 0.0004
## 240 0.6954 nan 0.0100 0.0000
## 260 0.6814 nan 0.0100 -0.0001
## 280 0.6658 nan 0.0100 0.0002
## 300 0.6535 nan 0.0100 -0.0000
## 320 0.6410 nan 0.0100 0.0003
## 340 0.6295 nan 0.0100 0.0000
## 360 0.6187 nan 0.0100 0.0001
## 380 0.6074 nan 0.0100 0.0000
## 400 0.5972 nan 0.0100 0.0001
## 420 0.5867 nan 0.0100 0.0002
## 440 0.5780 nan 0.0100 -0.0001
## 460 0.5686 nan 0.0100 -0.0002
## 480 0.5602 nan 0.0100 -0.0001
## 500 0.5519 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0038
## 2 1.3049 nan 0.0100 0.0033
## 3 1.2971 nan 0.0100 0.0032
## 4 1.2893 nan 0.0100 0.0031
## 5 1.2823 nan 0.0100 0.0033
## 6 1.2747 nan 0.0100 0.0034
## 7 1.2680 nan 0.0100 0.0032
## 8 1.2608 nan 0.0100 0.0035
## 9 1.2541 nan 0.0100 0.0028
## 10 1.2471 nan 0.0100 0.0028
## 20 1.1814 nan 0.0100 0.0025
## 40 1.0768 nan 0.0100 0.0019
## 60 0.9979 nan 0.0100 0.0015
## 80 0.9341 nan 0.0100 0.0012
## 100 0.8837 nan 0.0100 0.0008
## 120 0.8430 nan 0.0100 0.0006
## 140 0.8092 nan 0.0100 0.0005
## 160 0.7811 nan 0.0100 0.0004
## 180 0.7586 nan 0.0100 0.0001
## 200 0.7367 nan 0.0100 0.0001
## 220 0.7184 nan 0.0100 0.0002
## 240 0.7010 nan 0.0100 0.0003
## 260 0.6863 nan 0.0100 0.0001
## 280 0.6725 nan 0.0100 0.0002
## 300 0.6595 nan 0.0100 -0.0000
## 320 0.6475 nan 0.0100 0.0001
## 340 0.6363 nan 0.0100 0.0001
## 360 0.6250 nan 0.0100 -0.0000
## 380 0.6136 nan 0.0100 0.0000
## 400 0.6038 nan 0.0100 0.0001
## 420 0.5955 nan 0.0100 -0.0001
## 440 0.5865 nan 0.0100 0.0002
## 460 0.5781 nan 0.0100 -0.0000
## 480 0.5701 nan 0.0100 -0.0001
## 500 0.5626 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0044
## 2 1.3038 nan 0.0100 0.0037
## 3 1.2946 nan 0.0100 0.0042
## 4 1.2865 nan 0.0100 0.0037
## 5 1.2781 nan 0.0100 0.0036
## 6 1.2700 nan 0.0100 0.0034
## 7 1.2623 nan 0.0100 0.0030
## 8 1.2534 nan 0.0100 0.0037
## 9 1.2463 nan 0.0100 0.0032
## 10 1.2388 nan 0.0100 0.0031
## 20 1.1702 nan 0.0100 0.0026
## 40 1.0608 nan 0.0100 0.0022
## 60 0.9742 nan 0.0100 0.0015
## 80 0.9090 nan 0.0100 0.0012
## 100 0.8571 nan 0.0100 0.0008
## 120 0.8145 nan 0.0100 0.0006
## 140 0.7755 nan 0.0100 0.0005
## 160 0.7447 nan 0.0100 0.0005
## 180 0.7187 nan 0.0100 0.0004
## 200 0.6945 nan 0.0100 0.0002
## 220 0.6724 nan 0.0100 0.0002
## 240 0.6533 nan 0.0100 0.0001
## 260 0.6344 nan 0.0100 0.0002
## 280 0.6177 nan 0.0100 0.0000
## 300 0.6027 nan 0.0100 0.0000
## 320 0.5893 nan 0.0100 0.0000
## 340 0.5765 nan 0.0100 0.0001
## 360 0.5637 nan 0.0100 0.0002
## 380 0.5518 nan 0.0100 0.0001
## 400 0.5408 nan 0.0100 -0.0000
## 420 0.5296 nan 0.0100 -0.0000
## 440 0.5194 nan 0.0100 0.0001
## 460 0.5090 nan 0.0100 -0.0000
## 480 0.5000 nan 0.0100 -0.0001
## 500 0.4905 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3040 nan 0.0100 0.0034
## 3 1.2956 nan 0.0100 0.0038
## 4 1.2864 nan 0.0100 0.0044
## 5 1.2790 nan 0.0100 0.0034
## 6 1.2714 nan 0.0100 0.0035
## 7 1.2627 nan 0.0100 0.0040
## 8 1.2554 nan 0.0100 0.0031
## 9 1.2482 nan 0.0100 0.0029
## 10 1.2405 nan 0.0100 0.0035
## 20 1.1708 nan 0.0100 0.0032
## 40 1.0604 nan 0.0100 0.0023
## 60 0.9772 nan 0.0100 0.0018
## 80 0.9113 nan 0.0100 0.0012
## 100 0.8581 nan 0.0100 0.0009
## 120 0.8139 nan 0.0100 0.0008
## 140 0.7782 nan 0.0100 0.0005
## 160 0.7469 nan 0.0100 0.0004
## 180 0.7205 nan 0.0100 0.0003
## 200 0.6972 nan 0.0100 0.0002
## 220 0.6759 nan 0.0100 0.0001
## 240 0.6570 nan 0.0100 0.0004
## 260 0.6402 nan 0.0100 -0.0000
## 280 0.6250 nan 0.0100 0.0000
## 300 0.6109 nan 0.0100 -0.0001
## 320 0.5972 nan 0.0100 0.0002
## 340 0.5838 nan 0.0100 0.0004
## 360 0.5721 nan 0.0100 0.0003
## 380 0.5600 nan 0.0100 -0.0001
## 400 0.5491 nan 0.0100 0.0001
## 420 0.5393 nan 0.0100 0.0001
## 440 0.5289 nan 0.0100 -0.0001
## 460 0.5192 nan 0.0100 0.0000
## 480 0.5095 nan 0.0100 -0.0000
## 500 0.5005 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0038
## 2 1.3038 nan 0.0100 0.0035
## 3 1.2954 nan 0.0100 0.0033
## 4 1.2876 nan 0.0100 0.0033
## 5 1.2793 nan 0.0100 0.0035
## 6 1.2722 nan 0.0100 0.0033
## 7 1.2645 nan 0.0100 0.0035
## 8 1.2557 nan 0.0100 0.0041
## 9 1.2471 nan 0.0100 0.0039
## 10 1.2390 nan 0.0100 0.0033
## 20 1.1718 nan 0.0100 0.0029
## 40 1.0603 nan 0.0100 0.0021
## 60 0.9801 nan 0.0100 0.0016
## 80 0.9140 nan 0.0100 0.0010
## 100 0.8608 nan 0.0100 0.0008
## 120 0.8182 nan 0.0100 0.0007
## 140 0.7837 nan 0.0100 0.0005
## 160 0.7539 nan 0.0100 0.0002
## 180 0.7276 nan 0.0100 0.0003
## 200 0.7034 nan 0.0100 0.0003
## 220 0.6825 nan 0.0100 0.0002
## 240 0.6633 nan 0.0100 0.0001
## 260 0.6476 nan 0.0100 0.0001
## 280 0.6318 nan 0.0100 0.0001
## 300 0.6173 nan 0.0100 -0.0002
## 320 0.6036 nan 0.0100 0.0001
## 340 0.5909 nan 0.0100 -0.0001
## 360 0.5791 nan 0.0100 -0.0000
## 380 0.5680 nan 0.0100 0.0001
## 400 0.5577 nan 0.0100 -0.0000
## 420 0.5475 nan 0.0100 -0.0001
## 440 0.5371 nan 0.0100 0.0000
## 460 0.5272 nan 0.0100 -0.0001
## 480 0.5173 nan 0.0100 -0.0001
## 500 0.5072 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0039
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2866 nan 0.0100 0.0033
## 5 1.2771 nan 0.0100 0.0038
## 6 1.2688 nan 0.0100 0.0037
## 7 1.2607 nan 0.0100 0.0036
## 8 1.2528 nan 0.0100 0.0034
## 9 1.2443 nan 0.0100 0.0042
## 10 1.2359 nan 0.0100 0.0036
## 20 1.1631 nan 0.0100 0.0033
## 40 1.0477 nan 0.0100 0.0019
## 60 0.9605 nan 0.0100 0.0016
## 80 0.8927 nan 0.0100 0.0009
## 100 0.8355 nan 0.0100 0.0011
## 120 0.7902 nan 0.0100 0.0005
## 140 0.7516 nan 0.0100 0.0006
## 160 0.7171 nan 0.0100 0.0005
## 180 0.6882 nan 0.0100 0.0003
## 200 0.6625 nan 0.0100 0.0003
## 220 0.6398 nan 0.0100 0.0003
## 240 0.6190 nan 0.0100 0.0000
## 260 0.5993 nan 0.0100 0.0001
## 280 0.5817 nan 0.0100 -0.0001
## 300 0.5650 nan 0.0100 0.0001
## 320 0.5502 nan 0.0100 0.0001
## 340 0.5363 nan 0.0100 -0.0000
## 360 0.5233 nan 0.0100 0.0000
## 380 0.5096 nan 0.0100 -0.0001
## 400 0.4965 nan 0.0100 0.0000
## 420 0.4852 nan 0.0100 -0.0001
## 440 0.4741 nan 0.0100 0.0000
## 460 0.4627 nan 0.0100 0.0000
## 480 0.4523 nan 0.0100 -0.0000
## 500 0.4419 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0048
## 2 1.3029 nan 0.0100 0.0037
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2846 nan 0.0100 0.0038
## 5 1.2754 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0043
## 7 1.2584 nan 0.0100 0.0036
## 8 1.2508 nan 0.0100 0.0034
## 9 1.2418 nan 0.0100 0.0040
## 10 1.2339 nan 0.0100 0.0032
## 20 1.1629 nan 0.0100 0.0030
## 40 1.0495 nan 0.0100 0.0022
## 60 0.9608 nan 0.0100 0.0018
## 80 0.8916 nan 0.0100 0.0012
## 100 0.8383 nan 0.0100 0.0009
## 120 0.7937 nan 0.0100 0.0007
## 140 0.7566 nan 0.0100 0.0006
## 160 0.7232 nan 0.0100 0.0004
## 180 0.6944 nan 0.0100 0.0004
## 200 0.6705 nan 0.0100 0.0001
## 220 0.6494 nan 0.0100 0.0000
## 240 0.6283 nan 0.0100 0.0003
## 260 0.6087 nan 0.0100 -0.0000
## 280 0.5914 nan 0.0100 -0.0000
## 300 0.5756 nan 0.0100 0.0000
## 320 0.5611 nan 0.0100 -0.0001
## 340 0.5464 nan 0.0100 -0.0000
## 360 0.5338 nan 0.0100 -0.0000
## 380 0.5207 nan 0.0100 -0.0000
## 400 0.5079 nan 0.0100 -0.0002
## 420 0.4964 nan 0.0100 0.0000
## 440 0.4847 nan 0.0100 0.0001
## 460 0.4738 nan 0.0100 -0.0001
## 480 0.4637 nan 0.0100 0.0001
## 500 0.4532 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0037
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2961 nan 0.0100 0.0034
## 4 1.2880 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0037
## 6 1.2710 nan 0.0100 0.0039
## 7 1.2627 nan 0.0100 0.0034
## 8 1.2544 nan 0.0100 0.0039
## 9 1.2466 nan 0.0100 0.0034
## 10 1.2388 nan 0.0100 0.0038
## 20 1.1685 nan 0.0100 0.0026
## 40 1.0516 nan 0.0100 0.0024
## 60 0.9663 nan 0.0100 0.0017
## 80 0.8980 nan 0.0100 0.0010
## 100 0.8418 nan 0.0100 0.0008
## 120 0.7976 nan 0.0100 0.0008
## 140 0.7607 nan 0.0100 0.0006
## 160 0.7300 nan 0.0100 0.0003
## 180 0.7017 nan 0.0100 0.0005
## 200 0.6774 nan 0.0100 0.0001
## 220 0.6575 nan 0.0100 0.0001
## 240 0.6381 nan 0.0100 0.0002
## 260 0.6189 nan 0.0100 0.0003
## 280 0.6012 nan 0.0100 0.0000
## 300 0.5855 nan 0.0100 0.0005
## 320 0.5707 nan 0.0100 0.0002
## 340 0.5562 nan 0.0100 0.0001
## 360 0.5427 nan 0.0100 -0.0000
## 380 0.5296 nan 0.0100 0.0001
## 400 0.5170 nan 0.0100 0.0001
## 420 0.5059 nan 0.0100 0.0000
## 440 0.4948 nan 0.0100 -0.0002
## 460 0.4836 nan 0.0100 0.0001
## 480 0.4722 nan 0.0100 -0.0000
## 500 0.4625 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0401
## 2 1.1674 nan 0.1000 0.0311
## 3 1.1199 nan 0.1000 0.0173
## 4 1.0669 nan 0.1000 0.0223
## 5 1.0269 nan 0.1000 0.0177
## 6 0.9941 nan 0.1000 0.0127
## 7 0.9601 nan 0.1000 0.0157
## 8 0.9312 nan 0.1000 0.0117
## 9 0.9063 nan 0.1000 0.0085
## 10 0.8880 nan 0.1000 0.0071
## 20 0.7333 nan 0.1000 0.0049
## 40 0.5907 nan 0.1000 -0.0005
## 60 0.5052 nan 0.1000 -0.0018
## 80 0.4351 nan 0.1000 -0.0003
## 100 0.3833 nan 0.1000 -0.0009
## 120 0.3429 nan 0.1000 -0.0010
## 140 0.3058 nan 0.1000 0.0004
## 160 0.2714 nan 0.1000 -0.0002
## 180 0.2432 nan 0.1000 -0.0002
## 200 0.2183 nan 0.1000 -0.0004
## 220 0.1952 nan 0.1000 -0.0000
## 240 0.1751 nan 0.1000 -0.0005
## 260 0.1574 nan 0.1000 0.0000
## 280 0.1413 nan 0.1000 -0.0002
## 300 0.1276 nan 0.1000 -0.0006
## 320 0.1161 nan 0.1000 -0.0002
## 340 0.1066 nan 0.1000 -0.0001
## 360 0.0988 nan 0.1000 0.0001
## 380 0.0902 nan 0.1000 -0.0001
## 400 0.0819 nan 0.1000 -0.0002
## 420 0.0752 nan 0.1000 -0.0001
## 440 0.0690 nan 0.1000 -0.0003
## 460 0.0632 nan 0.1000 0.0001
## 480 0.0582 nan 0.1000 -0.0003
## 500 0.0533 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2442 nan 0.1000 0.0357
## 2 1.1832 nan 0.1000 0.0264
## 3 1.1289 nan 0.1000 0.0244
## 4 1.0775 nan 0.1000 0.0240
## 5 1.0320 nan 0.1000 0.0196
## 6 0.9941 nan 0.1000 0.0171
## 7 0.9614 nan 0.1000 0.0127
## 8 0.9324 nan 0.1000 0.0104
## 9 0.9084 nan 0.1000 0.0091
## 10 0.8821 nan 0.1000 0.0080
## 20 0.7443 nan 0.1000 0.0013
## 40 0.6162 nan 0.1000 -0.0018
## 60 0.5226 nan 0.1000 -0.0034
## 80 0.4514 nan 0.1000 0.0002
## 100 0.3994 nan 0.1000 -0.0014
## 120 0.3545 nan 0.1000 -0.0001
## 140 0.3176 nan 0.1000 -0.0009
## 160 0.2854 nan 0.1000 -0.0002
## 180 0.2552 nan 0.1000 0.0000
## 200 0.2288 nan 0.1000 -0.0003
## 220 0.2069 nan 0.1000 -0.0009
## 240 0.1861 nan 0.1000 -0.0004
## 260 0.1709 nan 0.1000 -0.0012
## 280 0.1572 nan 0.1000 -0.0006
## 300 0.1414 nan 0.1000 -0.0002
## 320 0.1298 nan 0.1000 -0.0003
## 340 0.1190 nan 0.1000 -0.0004
## 360 0.1097 nan 0.1000 -0.0003
## 380 0.1011 nan 0.1000 -0.0002
## 400 0.0934 nan 0.1000 -0.0004
## 420 0.0857 nan 0.1000 -0.0002
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## 460 0.0730 nan 0.1000 0.0001
## 480 0.0673 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0316
## 2 1.1806 nan 0.1000 0.0264
## 3 1.1271 nan 0.1000 0.0242
## 4 1.0771 nan 0.1000 0.0229
## 5 1.0351 nan 0.1000 0.0175
## 6 0.9959 nan 0.1000 0.0179
## 7 0.9596 nan 0.1000 0.0170
## 8 0.9322 nan 0.1000 0.0101
## 9 0.9097 nan 0.1000 0.0081
## 10 0.8848 nan 0.1000 0.0088
## 20 0.7486 nan 0.1000 0.0022
## 40 0.6187 nan 0.1000 -0.0015
## 60 0.5322 nan 0.1000 -0.0008
## 80 0.4675 nan 0.1000 0.0012
## 100 0.4131 nan 0.1000 -0.0002
## 120 0.3656 nan 0.1000 -0.0003
## 140 0.3288 nan 0.1000 -0.0008
## 160 0.2927 nan 0.1000 -0.0008
## 180 0.2652 nan 0.1000 -0.0009
## 200 0.2418 nan 0.1000 -0.0003
## 220 0.2200 nan 0.1000 -0.0005
## 240 0.2016 nan 0.1000 -0.0008
## 260 0.1860 nan 0.1000 -0.0013
## 280 0.1680 nan 0.1000 -0.0001
## 300 0.1547 nan 0.1000 -0.0006
## 320 0.1416 nan 0.1000 -0.0004
## 340 0.1306 nan 0.1000 -0.0003
## 360 0.1208 nan 0.1000 0.0003
## 380 0.1118 nan 0.1000 -0.0003
## 400 0.1030 nan 0.1000 -0.0003
## 420 0.0953 nan 0.1000 -0.0002
## 440 0.0872 nan 0.1000 0.0001
## 460 0.0798 nan 0.1000 -0.0002
## 480 0.0742 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0418
## 2 1.1607 nan 0.1000 0.0262
## 3 1.1056 nan 0.1000 0.0179
## 4 1.0527 nan 0.1000 0.0234
## 5 1.0120 nan 0.1000 0.0155
## 6 0.9715 nan 0.1000 0.0171
## 7 0.9378 nan 0.1000 0.0132
## 8 0.9013 nan 0.1000 0.0151
## 9 0.8768 nan 0.1000 0.0063
## 10 0.8572 nan 0.1000 0.0070
## 20 0.6932 nan 0.1000 0.0031
## 40 0.5408 nan 0.1000 0.0002
## 60 0.4604 nan 0.1000 -0.0014
## 80 0.3817 nan 0.1000 -0.0015
## 100 0.3252 nan 0.1000 0.0001
## 120 0.2802 nan 0.1000 -0.0008
## 140 0.2428 nan 0.1000 -0.0004
## 160 0.2098 nan 0.1000 -0.0006
## 180 0.1821 nan 0.1000 0.0002
## 200 0.1603 nan 0.1000 -0.0002
## 220 0.1396 nan 0.1000 0.0001
## 240 0.1226 nan 0.1000 -0.0005
## 260 0.1083 nan 0.1000 -0.0003
## 280 0.0966 nan 0.1000 -0.0003
## 300 0.0873 nan 0.1000 -0.0002
## 320 0.0789 nan 0.1000 -0.0002
## 340 0.0701 nan 0.1000 0.0001
## 360 0.0624 nan 0.1000 -0.0000
## 380 0.0561 nan 0.1000 -0.0001
## 400 0.0507 nan 0.1000 -0.0001
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## 460 0.0367 nan 0.1000 -0.0001
## 480 0.0328 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2302 nan 0.1000 0.0435
## 2 1.1650 nan 0.1000 0.0295
## 3 1.1131 nan 0.1000 0.0217
## 4 1.0603 nan 0.1000 0.0229
## 5 1.0176 nan 0.1000 0.0204
## 6 0.9806 nan 0.1000 0.0145
## 7 0.9426 nan 0.1000 0.0168
## 8 0.9123 nan 0.1000 0.0138
## 9 0.8847 nan 0.1000 0.0108
## 10 0.8552 nan 0.1000 0.0106
## 20 0.7055 nan 0.1000 0.0001
## 40 0.5540 nan 0.1000 0.0029
## 60 0.4641 nan 0.1000 -0.0013
## 80 0.3881 nan 0.1000 -0.0009
## 100 0.3293 nan 0.1000 -0.0007
## 120 0.2851 nan 0.1000 -0.0007
## 140 0.2461 nan 0.1000 -0.0011
## 160 0.2174 nan 0.1000 -0.0001
## 180 0.1863 nan 0.1000 -0.0008
## 200 0.1639 nan 0.1000 -0.0002
## 220 0.1451 nan 0.1000 -0.0003
## 240 0.1284 nan 0.1000 -0.0002
## 260 0.1148 nan 0.1000 -0.0002
## 280 0.1008 nan 0.1000 -0.0004
## 300 0.0905 nan 0.1000 -0.0001
## 320 0.0793 nan 0.1000 -0.0001
## 340 0.0708 nan 0.1000 -0.0002
## 360 0.0632 nan 0.1000 -0.0003
## 380 0.0559 nan 0.1000 -0.0001
## 400 0.0506 nan 0.1000 -0.0002
## 420 0.0461 nan 0.1000 -0.0002
## 440 0.0414 nan 0.1000 -0.0001
## 460 0.0374 nan 0.1000 -0.0001
## 480 0.0336 nan 0.1000 -0.0000
## 500 0.0303 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0360
## 2 1.1636 nan 0.1000 0.0357
## 3 1.1068 nan 0.1000 0.0253
## 4 1.0531 nan 0.1000 0.0252
## 5 1.0096 nan 0.1000 0.0190
## 6 0.9771 nan 0.1000 0.0142
## 7 0.9412 nan 0.1000 0.0121
## 8 0.9112 nan 0.1000 0.0128
## 9 0.8846 nan 0.1000 0.0091
## 10 0.8576 nan 0.1000 0.0107
## 20 0.7034 nan 0.1000 0.0048
## 40 0.5497 nan 0.1000 0.0007
## 60 0.4685 nan 0.1000 -0.0003
## 80 0.4007 nan 0.1000 -0.0011
## 100 0.3477 nan 0.1000 -0.0013
## 120 0.3014 nan 0.1000 -0.0003
## 140 0.2625 nan 0.1000 -0.0006
## 160 0.2328 nan 0.1000 -0.0005
## 180 0.2099 nan 0.1000 -0.0006
## 200 0.1850 nan 0.1000 -0.0009
## 220 0.1627 nan 0.1000 -0.0005
## 240 0.1452 nan 0.1000 -0.0002
## 260 0.1309 nan 0.1000 -0.0004
## 280 0.1170 nan 0.1000 -0.0004
## 300 0.1048 nan 0.1000 -0.0006
## 320 0.0939 nan 0.1000 -0.0004
## 340 0.0850 nan 0.1000 -0.0002
## 360 0.0764 nan 0.1000 -0.0002
## 380 0.0679 nan 0.1000 -0.0001
## 400 0.0606 nan 0.1000 -0.0003
## 420 0.0550 nan 0.1000 -0.0001
## 440 0.0500 nan 0.1000 -0.0002
## 460 0.0453 nan 0.1000 -0.0000
## 480 0.0405 nan 0.1000 -0.0002
## 500 0.0364 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0361
## 2 1.1540 nan 0.1000 0.0367
## 3 1.0851 nan 0.1000 0.0297
## 4 1.0349 nan 0.1000 0.0189
## 5 0.9886 nan 0.1000 0.0174
## 6 0.9506 nan 0.1000 0.0141
## 7 0.9125 nan 0.1000 0.0150
## 8 0.8775 nan 0.1000 0.0145
## 9 0.8477 nan 0.1000 0.0103
## 10 0.8224 nan 0.1000 0.0079
## 20 0.6541 nan 0.1000 -0.0011
## 40 0.4871 nan 0.1000 -0.0006
## 60 0.3957 nan 0.1000 -0.0014
## 80 0.3381 nan 0.1000 -0.0013
## 100 0.2759 nan 0.1000 -0.0001
## 120 0.2369 nan 0.1000 -0.0009
## 140 0.2025 nan 0.1000 -0.0003
## 160 0.1712 nan 0.1000 -0.0001
## 180 0.1478 nan 0.1000 -0.0004
## 200 0.1285 nan 0.1000 -0.0004
## 220 0.1099 nan 0.1000 -0.0002
## 240 0.0949 nan 0.1000 -0.0003
## 260 0.0823 nan 0.1000 -0.0004
## 280 0.0718 nan 0.1000 -0.0001
## 300 0.0634 nan 0.1000 -0.0002
## 320 0.0549 nan 0.1000 -0.0003
## 340 0.0485 nan 0.1000 -0.0000
## 360 0.0421 nan 0.1000 -0.0001
## 380 0.0374 nan 0.1000 -0.0001
## 400 0.0332 nan 0.1000 -0.0000
## 420 0.0294 nan 0.1000 -0.0000
## 440 0.0259 nan 0.1000 -0.0001
## 460 0.0229 nan 0.1000 -0.0001
## 480 0.0202 nan 0.1000 -0.0000
## 500 0.0180 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0378
## 2 1.1544 nan 0.1000 0.0309
## 3 1.0967 nan 0.1000 0.0214
## 4 1.0418 nan 0.1000 0.0235
## 5 0.9973 nan 0.1000 0.0172
## 6 0.9544 nan 0.1000 0.0162
## 7 0.9188 nan 0.1000 0.0141
## 8 0.8855 nan 0.1000 0.0121
## 9 0.8542 nan 0.1000 0.0111
## 10 0.8302 nan 0.1000 0.0092
## 20 0.6667 nan 0.1000 0.0027
## 40 0.5060 nan 0.1000 -0.0001
## 60 0.4009 nan 0.1000 0.0011
## 80 0.3305 nan 0.1000 -0.0001
## 100 0.2756 nan 0.1000 -0.0001
## 120 0.2333 nan 0.1000 -0.0005
## 140 0.1999 nan 0.1000 -0.0005
## 160 0.1697 nan 0.1000 -0.0001
## 180 0.1449 nan 0.1000 -0.0005
## 200 0.1228 nan 0.1000 -0.0003
## 220 0.1062 nan 0.1000 -0.0003
## 240 0.0917 nan 0.1000 -0.0002
## 260 0.0802 nan 0.1000 -0.0004
## 280 0.0704 nan 0.1000 -0.0003
## 300 0.0621 nan 0.1000 -0.0002
## 320 0.0538 nan 0.1000 -0.0002
## 340 0.0471 nan 0.1000 -0.0000
## 360 0.0413 nan 0.1000 -0.0001
## 380 0.0361 nan 0.1000 -0.0002
## 400 0.0317 nan 0.1000 -0.0000
## 420 0.0281 nan 0.1000 -0.0001
## 440 0.0245 nan 0.1000 -0.0000
## 460 0.0217 nan 0.1000 -0.0000
## 480 0.0195 nan 0.1000 -0.0001
## 500 0.0173 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0403
## 2 1.1658 nan 0.1000 0.0345
## 3 1.1071 nan 0.1000 0.0246
## 4 1.0520 nan 0.1000 0.0245
## 5 1.0014 nan 0.1000 0.0232
## 6 0.9603 nan 0.1000 0.0155
## 7 0.9261 nan 0.1000 0.0153
## 8 0.8930 nan 0.1000 0.0120
## 9 0.8614 nan 0.1000 0.0103
## 10 0.8401 nan 0.1000 0.0065
## 20 0.6902 nan 0.1000 0.0011
## 40 0.5086 nan 0.1000 0.0016
## 60 0.4168 nan 0.1000 -0.0002
## 80 0.3509 nan 0.1000 -0.0004
## 100 0.2943 nan 0.1000 -0.0005
## 120 0.2485 nan 0.1000 0.0003
## 140 0.2117 nan 0.1000 -0.0020
## 160 0.1805 nan 0.1000 -0.0006
## 180 0.1541 nan 0.1000 -0.0006
## 200 0.1340 nan 0.1000 -0.0007
## 220 0.1158 nan 0.1000 -0.0002
## 240 0.1021 nan 0.1000 -0.0003
## 260 0.0898 nan 0.1000 -0.0002
## 280 0.0790 nan 0.1000 -0.0007
## 300 0.0686 nan 0.1000 -0.0003
## 320 0.0608 nan 0.1000 -0.0000
## 340 0.0539 nan 0.1000 -0.0004
## 360 0.0472 nan 0.1000 -0.0003
## 380 0.0417 nan 0.1000 -0.0002
## 400 0.0368 nan 0.1000 -0.0001
## 420 0.0326 nan 0.1000 -0.0001
## 440 0.0288 nan 0.1000 -0.0001
## 460 0.0253 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0000
## 500 0.0200 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0003
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2709 nan 0.0010 0.0004
## 80 1.2550 nan 0.0010 0.0004
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1979 nan 0.0010 0.0003
## 180 1.1846 nan 0.0010 0.0003
## 200 1.1713 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0002
## 240 1.1466 nan 0.0010 0.0003
## 260 1.1347 nan 0.0010 0.0003
## 280 1.1232 nan 0.0010 0.0003
## 300 1.1118 nan 0.0010 0.0002
## 320 1.1007 nan 0.0010 0.0002
## 340 1.0900 nan 0.0010 0.0002
## 360 1.0800 nan 0.0010 0.0002
## 380 1.0699 nan 0.0010 0.0002
## 400 1.0603 nan 0.0010 0.0002
## 420 1.0507 nan 0.0010 0.0002
## 440 1.0415 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0239 nan 0.0010 0.0002
## 500 1.0154 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2713 nan 0.0010 0.0003
## 80 1.2556 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0003
## 120 1.2262 nan 0.0010 0.0003
## 140 1.2121 nan 0.0010 0.0003
## 160 1.1984 nan 0.0010 0.0003
## 180 1.1852 nan 0.0010 0.0003
## 200 1.1722 nan 0.0010 0.0003
## 220 1.1594 nan 0.0010 0.0003
## 240 1.1472 nan 0.0010 0.0003
## 260 1.1355 nan 0.0010 0.0003
## 280 1.1241 nan 0.0010 0.0002
## 300 1.1128 nan 0.0010 0.0003
## 320 1.1020 nan 0.0010 0.0002
## 340 1.0915 nan 0.0010 0.0003
## 360 1.0812 nan 0.0010 0.0002
## 380 1.0713 nan 0.0010 0.0002
## 400 1.0613 nan 0.0010 0.0002
## 420 1.0518 nan 0.0010 0.0002
## 440 1.0425 nan 0.0010 0.0002
## 460 1.0335 nan 0.0010 0.0002
## 480 1.0249 nan 0.0010 0.0002
## 500 1.0164 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0003
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0003
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2414 nan 0.0010 0.0004
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2131 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0003
## 180 1.1860 nan 0.0010 0.0003
## 200 1.1732 nan 0.0010 0.0003
## 220 1.1606 nan 0.0010 0.0003
## 240 1.1483 nan 0.0010 0.0003
## 260 1.1364 nan 0.0010 0.0003
## 280 1.1250 nan 0.0010 0.0003
## 300 1.1138 nan 0.0010 0.0002
## 320 1.1030 nan 0.0010 0.0002
## 340 1.0926 nan 0.0010 0.0002
## 360 1.0825 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0533 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0351 nan 0.0010 0.0002
## 480 1.0264 nan 0.0010 0.0002
## 500 1.0178 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0005
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0003
## 80 1.2510 nan 0.0010 0.0003
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2189 nan 0.0010 0.0004
## 140 1.2037 nan 0.0010 0.0003
## 160 1.1891 nan 0.0010 0.0003
## 180 1.1749 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1477 nan 0.0010 0.0003
## 240 1.1348 nan 0.0010 0.0003
## 260 1.1222 nan 0.0010 0.0003
## 280 1.1101 nan 0.0010 0.0003
## 300 1.0982 nan 0.0010 0.0002
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0003
## 360 1.0643 nan 0.0010 0.0002
## 380 1.0536 nan 0.0010 0.0002
## 400 1.0432 nan 0.0010 0.0002
## 420 1.0334 nan 0.0010 0.0002
## 440 1.0237 nan 0.0010 0.0002
## 460 1.0140 nan 0.0010 0.0002
## 480 1.0049 nan 0.0010 0.0002
## 500 0.9957 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2675 nan 0.0010 0.0004
## 80 1.2511 nan 0.0010 0.0004
## 100 1.2353 nan 0.0010 0.0004
## 120 1.2197 nan 0.0010 0.0003
## 140 1.2047 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0003
## 180 1.1761 nan 0.0010 0.0003
## 200 1.1624 nan 0.0010 0.0003
## 220 1.1490 nan 0.0010 0.0003
## 240 1.1360 nan 0.0010 0.0003
## 260 1.1235 nan 0.0010 0.0002
## 280 1.1115 nan 0.0010 0.0003
## 300 1.0996 nan 0.0010 0.0003
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0773 nan 0.0010 0.0002
## 360 1.0663 nan 0.0010 0.0002
## 380 1.0561 nan 0.0010 0.0002
## 400 1.0455 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0256 nan 0.0010 0.0002
## 460 1.0160 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
## 500 0.9976 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0003
## 100 1.2358 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0004
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1908 nan 0.0010 0.0003
## 180 1.1766 nan 0.0010 0.0003
## 200 1.1631 nan 0.0010 0.0003
## 220 1.1499 nan 0.0010 0.0003
## 240 1.1371 nan 0.0010 0.0003
## 260 1.1246 nan 0.0010 0.0003
## 280 1.1127 nan 0.0010 0.0003
## 300 1.1011 nan 0.0010 0.0002
## 320 1.0896 nan 0.0010 0.0003
## 340 1.0787 nan 0.0010 0.0002
## 360 1.0680 nan 0.0010 0.0002
## 380 1.0577 nan 0.0010 0.0002
## 400 1.0476 nan 0.0010 0.0002
## 420 1.0376 nan 0.0010 0.0002
## 440 1.0279 nan 0.0010 0.0001
## 460 1.0183 nan 0.0010 0.0002
## 480 1.0090 nan 0.0010 0.0002
## 500 1.0002 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0005
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2829 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0004
## 100 1.2299 nan 0.0010 0.0004
## 120 1.2132 nan 0.0010 0.0004
## 140 1.1977 nan 0.0010 0.0003
## 160 1.1825 nan 0.0010 0.0003
## 180 1.1679 nan 0.0010 0.0003
## 200 1.1535 nan 0.0010 0.0003
## 220 1.1395 nan 0.0010 0.0003
## 240 1.1262 nan 0.0010 0.0003
## 260 1.1128 nan 0.0010 0.0003
## 280 1.1000 nan 0.0010 0.0003
## 300 1.0876 nan 0.0010 0.0003
## 320 1.0757 nan 0.0010 0.0003
## 340 1.0641 nan 0.0010 0.0002
## 360 1.0526 nan 0.0010 0.0003
## 380 1.0416 nan 0.0010 0.0002
## 400 1.0308 nan 0.0010 0.0002
## 420 1.0203 nan 0.0010 0.0002
## 440 1.0101 nan 0.0010 0.0002
## 460 1.0000 nan 0.0010 0.0002
## 480 0.9901 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0005
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2476 nan 0.0010 0.0004
## 100 1.2311 nan 0.0010 0.0004
## 120 1.2150 nan 0.0010 0.0004
## 140 1.1992 nan 0.0010 0.0003
## 160 1.1839 nan 0.0010 0.0003
## 180 1.1692 nan 0.0010 0.0003
## 200 1.1545 nan 0.0010 0.0003
## 220 1.1408 nan 0.0010 0.0003
## 240 1.1274 nan 0.0010 0.0003
## 260 1.1144 nan 0.0010 0.0003
## 280 1.1015 nan 0.0010 0.0003
## 300 1.0894 nan 0.0010 0.0003
## 320 1.0773 nan 0.0010 0.0003
## 340 1.0658 nan 0.0010 0.0003
## 360 1.0545 nan 0.0010 0.0002
## 380 1.0436 nan 0.0010 0.0002
## 400 1.0331 nan 0.0010 0.0002
## 420 1.0227 nan 0.0010 0.0002
## 440 1.0127 nan 0.0010 0.0002
## 460 1.0028 nan 0.0010 0.0002
## 480 0.9931 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3108 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0005
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0003
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2165 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
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## 180 1.1710 nan 0.0010 0.0003
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## 320 1.0800 nan 0.0010 0.0003
## 340 1.0686 nan 0.0010 0.0003
## 360 1.0575 nan 0.0010 0.0002
## 380 1.0467 nan 0.0010 0.0002
## 400 1.0361 nan 0.0010 0.0002
## 420 1.0259 nan 0.0010 0.0002
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## 460 1.0061 nan 0.0010 0.0002
## 480 0.9966 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0041
## 2 1.3040 nan 0.0100 0.0038
## 3 1.2960 nan 0.0100 0.0042
## 4 1.2877 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0032
## 6 1.2716 nan 0.0100 0.0038
## 7 1.2630 nan 0.0100 0.0037
## 8 1.2550 nan 0.0100 0.0038
## 9 1.2470 nan 0.0100 0.0035
## 10 1.2392 nan 0.0100 0.0037
## 20 1.1720 nan 0.0100 0.0030
## 40 1.0611 nan 0.0100 0.0021
## 60 0.9745 nan 0.0100 0.0015
## 80 0.9067 nan 0.0100 0.0012
## 100 0.8507 nan 0.0100 0.0010
## 120 0.8060 nan 0.0100 0.0008
## 140 0.7694 nan 0.0100 0.0005
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## 180 0.7128 nan 0.0100 0.0003
## 200 0.6898 nan 0.0100 0.0002
## 220 0.6696 nan 0.0100 0.0000
## 240 0.6510 nan 0.0100 0.0001
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## 280 0.6196 nan 0.0100 -0.0001
## 300 0.6061 nan 0.0100 0.0001
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## 340 0.5814 nan 0.0100 -0.0000
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## 380 0.5594 nan 0.0100 0.0001
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## 440 0.5310 nan 0.0100 -0.0000
## 460 0.5224 nan 0.0100 0.0000
## 480 0.5138 nan 0.0100 -0.0001
## 500 0.5058 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2931 nan 0.0100 0.0038
## 4 1.2850 nan 0.0100 0.0035
## 5 1.2768 nan 0.0100 0.0036
## 6 1.2694 nan 0.0100 0.0033
## 7 1.2608 nan 0.0100 0.0040
## 8 1.2531 nan 0.0100 0.0037
## 9 1.2450 nan 0.0100 0.0037
## 10 1.2382 nan 0.0100 0.0032
## 20 1.1716 nan 0.0100 0.0026
## 40 1.0607 nan 0.0100 0.0021
## 60 0.9749 nan 0.0100 0.0017
## 80 0.9080 nan 0.0100 0.0011
## 100 0.8561 nan 0.0100 0.0010
## 120 0.8112 nan 0.0100 0.0008
## 140 0.7759 nan 0.0100 0.0004
## 160 0.7450 nan 0.0100 0.0002
## 180 0.7181 nan 0.0100 0.0003
## 200 0.6964 nan 0.0100 0.0004
## 220 0.6759 nan 0.0100 0.0001
## 240 0.6582 nan 0.0100 0.0001
## 260 0.6423 nan 0.0100 -0.0001
## 280 0.6277 nan 0.0100 0.0000
## 300 0.6136 nan 0.0100 0.0001
## 320 0.6017 nan 0.0100 0.0002
## 340 0.5903 nan 0.0100 -0.0002
## 360 0.5792 nan 0.0100 0.0000
## 380 0.5690 nan 0.0100 -0.0000
## 400 0.5589 nan 0.0100 -0.0001
## 420 0.5494 nan 0.0100 0.0000
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## 480 0.5256 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0039
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2874 nan 0.0100 0.0039
## 5 1.2790 nan 0.0100 0.0037
## 6 1.2715 nan 0.0100 0.0036
## 7 1.2640 nan 0.0100 0.0034
## 8 1.2561 nan 0.0100 0.0034
## 9 1.2492 nan 0.0100 0.0030
## 10 1.2412 nan 0.0100 0.0038
## 20 1.1725 nan 0.0100 0.0025
## 40 1.0642 nan 0.0100 0.0023
## 60 0.9782 nan 0.0100 0.0017
## 80 0.9121 nan 0.0100 0.0010
## 100 0.8589 nan 0.0100 0.0011
## 120 0.8147 nan 0.0100 0.0005
## 140 0.7771 nan 0.0100 0.0007
## 160 0.7462 nan 0.0100 0.0005
## 180 0.7204 nan 0.0100 0.0001
## 200 0.6993 nan 0.0100 0.0001
## 220 0.6790 nan 0.0100 0.0002
## 240 0.6614 nan 0.0100 0.0001
## 260 0.6459 nan 0.0100 0.0001
## 280 0.6317 nan 0.0100 0.0001
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## 320 0.6070 nan 0.0100 0.0000
## 340 0.5954 nan 0.0100 0.0001
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## 380 0.5742 nan 0.0100 -0.0001
## 400 0.5648 nan 0.0100 -0.0000
## 420 0.5559 nan 0.0100 -0.0002
## 440 0.5474 nan 0.0100 -0.0002
## 460 0.5382 nan 0.0100 -0.0000
## 480 0.5312 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0045
## 2 1.3011 nan 0.0100 0.0045
## 3 1.2920 nan 0.0100 0.0041
## 4 1.2835 nan 0.0100 0.0037
## 5 1.2758 nan 0.0100 0.0035
## 6 1.2668 nan 0.0100 0.0037
## 7 1.2582 nan 0.0100 0.0038
## 8 1.2506 nan 0.0100 0.0038
## 9 1.2418 nan 0.0100 0.0042
## 10 1.2338 nan 0.0100 0.0034
## 20 1.1611 nan 0.0100 0.0029
## 40 1.0431 nan 0.0100 0.0022
## 60 0.9529 nan 0.0100 0.0017
## 80 0.8817 nan 0.0100 0.0012
## 100 0.8263 nan 0.0100 0.0009
## 120 0.7801 nan 0.0100 0.0008
## 140 0.7410 nan 0.0100 0.0006
## 160 0.7087 nan 0.0100 0.0005
## 180 0.6812 nan 0.0100 0.0003
## 200 0.6576 nan 0.0100 0.0001
## 220 0.6365 nan 0.0100 0.0002
## 240 0.6168 nan 0.0100 0.0000
## 260 0.5990 nan 0.0100 0.0000
## 280 0.5826 nan 0.0100 0.0000
## 300 0.5678 nan 0.0100 0.0001
## 320 0.5549 nan 0.0100 -0.0000
## 340 0.5432 nan 0.0100 -0.0000
## 360 0.5307 nan 0.0100 -0.0001
## 380 0.5198 nan 0.0100 0.0000
## 400 0.5084 nan 0.0100 -0.0000
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## 440 0.4874 nan 0.0100 0.0001
## 460 0.4782 nan 0.0100 0.0000
## 480 0.4695 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0038
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2941 nan 0.0100 0.0042
## 4 1.2859 nan 0.0100 0.0041
## 5 1.2767 nan 0.0100 0.0042
## 6 1.2685 nan 0.0100 0.0040
## 7 1.2606 nan 0.0100 0.0037
## 8 1.2520 nan 0.0100 0.0040
## 9 1.2433 nan 0.0100 0.0040
## 10 1.2356 nan 0.0100 0.0033
## 20 1.1621 nan 0.0100 0.0027
## 40 1.0441 nan 0.0100 0.0021
## 60 0.9532 nan 0.0100 0.0018
## 80 0.8826 nan 0.0100 0.0012
## 100 0.8248 nan 0.0100 0.0010
## 120 0.7784 nan 0.0100 0.0009
## 140 0.7399 nan 0.0100 0.0007
## 160 0.7076 nan 0.0100 0.0004
## 180 0.6807 nan 0.0100 0.0003
## 200 0.6559 nan 0.0100 0.0002
## 220 0.6343 nan 0.0100 0.0001
## 240 0.6163 nan 0.0100 0.0002
## 260 0.5987 nan 0.0100 0.0001
## 280 0.5835 nan 0.0100 -0.0002
## 300 0.5695 nan 0.0100 0.0001
## 320 0.5561 nan 0.0100 0.0001
## 340 0.5444 nan 0.0100 0.0001
## 360 0.5330 nan 0.0100 -0.0000
## 380 0.5222 nan 0.0100 -0.0001
## 400 0.5115 nan 0.0100 -0.0002
## 420 0.5009 nan 0.0100 -0.0000
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## 480 0.4724 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0044
## 3 1.2928 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0037
## 5 1.2760 nan 0.0100 0.0040
## 6 1.2675 nan 0.0100 0.0041
## 7 1.2593 nan 0.0100 0.0037
## 8 1.2509 nan 0.0100 0.0038
## 9 1.2429 nan 0.0100 0.0034
## 10 1.2351 nan 0.0100 0.0035
## 20 1.1632 nan 0.0100 0.0030
## 40 1.0469 nan 0.0100 0.0022
## 60 0.9573 nan 0.0100 0.0018
## 80 0.8877 nan 0.0100 0.0012
## 100 0.8332 nan 0.0100 0.0007
## 120 0.7882 nan 0.0100 0.0009
## 140 0.7500 nan 0.0100 0.0004
## 160 0.7186 nan 0.0100 0.0003
## 180 0.6914 nan 0.0100 0.0002
## 200 0.6666 nan 0.0100 0.0004
## 220 0.6452 nan 0.0100 0.0002
## 240 0.6271 nan 0.0100 -0.0000
## 260 0.6106 nan 0.0100 -0.0000
## 280 0.5955 nan 0.0100 0.0000
## 300 0.5818 nan 0.0100 0.0001
## 320 0.5674 nan 0.0100 0.0001
## 340 0.5550 nan 0.0100 0.0000
## 360 0.5434 nan 0.0100 -0.0001
## 380 0.5321 nan 0.0100 0.0000
## 400 0.5214 nan 0.0100 -0.0001
## 420 0.5121 nan 0.0100 -0.0002
## 440 0.5025 nan 0.0100 -0.0001
## 460 0.4926 nan 0.0100 -0.0001
## 480 0.4844 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0039
## 2 1.3010 nan 0.0100 0.0048
## 3 1.2919 nan 0.0100 0.0043
## 4 1.2829 nan 0.0100 0.0041
## 5 1.2732 nan 0.0100 0.0044
## 6 1.2640 nan 0.0100 0.0041
## 7 1.2556 nan 0.0100 0.0039
## 8 1.2467 nan 0.0100 0.0041
## 9 1.2379 nan 0.0100 0.0036
## 10 1.2301 nan 0.0100 0.0035
## 20 1.1519 nan 0.0100 0.0030
## 40 1.0295 nan 0.0100 0.0024
## 60 0.9356 nan 0.0100 0.0015
## 80 0.8625 nan 0.0100 0.0012
## 100 0.8019 nan 0.0100 0.0011
## 120 0.7514 nan 0.0100 0.0006
## 140 0.7116 nan 0.0100 0.0006
## 160 0.6768 nan 0.0100 0.0003
## 180 0.6479 nan 0.0100 0.0004
## 200 0.6224 nan 0.0100 0.0001
## 220 0.5992 nan 0.0100 0.0002
## 240 0.5800 nan 0.0100 0.0001
## 260 0.5624 nan 0.0100 0.0000
## 280 0.5446 nan 0.0100 0.0002
## 300 0.5280 nan 0.0100 0.0001
## 320 0.5129 nan 0.0100 0.0002
## 340 0.4998 nan 0.0100 -0.0001
## 360 0.4874 nan 0.0100 -0.0001
## 380 0.4749 nan 0.0100 -0.0001
## 400 0.4636 nan 0.0100 0.0000
## 420 0.4527 nan 0.0100 0.0001
## 440 0.4406 nan 0.0100 -0.0000
## 460 0.4310 nan 0.0100 -0.0001
## 480 0.4200 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0037
## 3 1.2924 nan 0.0100 0.0041
## 4 1.2835 nan 0.0100 0.0036
## 5 1.2743 nan 0.0100 0.0041
## 6 1.2654 nan 0.0100 0.0039
## 7 1.2571 nan 0.0100 0.0039
## 8 1.2486 nan 0.0100 0.0040
## 9 1.2407 nan 0.0100 0.0035
## 10 1.2321 nan 0.0100 0.0042
## 20 1.1569 nan 0.0100 0.0031
## 40 1.0343 nan 0.0100 0.0024
## 60 0.9400 nan 0.0100 0.0017
## 80 0.8684 nan 0.0100 0.0012
## 100 0.8104 nan 0.0100 0.0009
## 120 0.7619 nan 0.0100 0.0009
## 140 0.7211 nan 0.0100 0.0004
## 160 0.6863 nan 0.0100 0.0006
## 180 0.6579 nan 0.0100 0.0004
## 200 0.6335 nan 0.0100 0.0005
## 220 0.6112 nan 0.0100 0.0000
## 240 0.5914 nan 0.0100 0.0001
## 260 0.5723 nan 0.0100 -0.0001
## 280 0.5559 nan 0.0100 0.0000
## 300 0.5411 nan 0.0100 0.0001
## 320 0.5277 nan 0.0100 -0.0001
## 340 0.5134 nan 0.0100 0.0002
## 360 0.5003 nan 0.0100 0.0001
## 380 0.4878 nan 0.0100 -0.0000
## 400 0.4761 nan 0.0100 -0.0001
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## 440 0.4541 nan 0.0100 0.0000
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## 480 0.4339 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0042
## 2 1.3012 nan 0.0100 0.0042
## 3 1.2916 nan 0.0100 0.0044
## 4 1.2821 nan 0.0100 0.0044
## 5 1.2737 nan 0.0100 0.0039
## 6 1.2651 nan 0.0100 0.0037
## 7 1.2561 nan 0.0100 0.0042
## 8 1.2475 nan 0.0100 0.0040
## 9 1.2398 nan 0.0100 0.0031
## 10 1.2314 nan 0.0100 0.0037
## 20 1.1553 nan 0.0100 0.0033
## 40 1.0331 nan 0.0100 0.0021
## 60 0.9403 nan 0.0100 0.0015
## 80 0.8679 nan 0.0100 0.0012
## 100 0.8103 nan 0.0100 0.0014
## 120 0.7644 nan 0.0100 0.0007
## 140 0.7260 nan 0.0100 0.0007
## 160 0.6930 nan 0.0100 0.0004
## 180 0.6654 nan 0.0100 0.0003
## 200 0.6408 nan 0.0100 0.0003
## 220 0.6188 nan 0.0100 0.0001
## 240 0.5986 nan 0.0100 0.0002
## 260 0.5809 nan 0.0100 0.0001
## 280 0.5645 nan 0.0100 0.0000
## 300 0.5497 nan 0.0100 0.0000
## 320 0.5347 nan 0.0100 -0.0000
## 340 0.5215 nan 0.0100 0.0000
## 360 0.5085 nan 0.0100 -0.0000
## 380 0.4955 nan 0.0100 -0.0000
## 400 0.4841 nan 0.0100 0.0001
## 420 0.4739 nan 0.0100 0.0000
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## 460 0.4534 nan 0.0100 0.0001
## 480 0.4435 nan 0.0100 -0.0001
## 500 0.4338 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2312 nan 0.1000 0.0359
## 2 1.1616 nan 0.1000 0.0330
## 3 1.1025 nan 0.1000 0.0257
## 4 1.0514 nan 0.1000 0.0209
## 5 1.0030 nan 0.1000 0.0187
## 6 0.9638 nan 0.1000 0.0154
## 7 0.9298 nan 0.1000 0.0110
## 8 0.9016 nan 0.1000 0.0104
## 9 0.8767 nan 0.1000 0.0104
## 10 0.8546 nan 0.1000 0.0083
## 20 0.6865 nan 0.1000 0.0038
## 40 0.5493 nan 0.1000 -0.0001
## 60 0.4713 nan 0.1000 -0.0011
## 80 0.4040 nan 0.1000 -0.0010
## 100 0.3571 nan 0.1000 0.0001
## 120 0.3154 nan 0.1000 -0.0002
## 140 0.2807 nan 0.1000 -0.0008
## 160 0.2489 nan 0.1000 -0.0004
## 180 0.2225 nan 0.1000 -0.0010
## 200 0.2002 nan 0.1000 -0.0002
## 220 0.1824 nan 0.1000 -0.0008
## 240 0.1638 nan 0.1000 -0.0003
## 260 0.1506 nan 0.1000 -0.0003
## 280 0.1381 nan 0.1000 -0.0003
## 300 0.1271 nan 0.1000 -0.0004
## 320 0.1159 nan 0.1000 -0.0001
## 340 0.1058 nan 0.1000 -0.0003
## 360 0.0964 nan 0.1000 -0.0001
## 380 0.0891 nan 0.1000 -0.0002
## 400 0.0819 nan 0.1000 -0.0002
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## 440 0.0690 nan 0.1000 0.0000
## 460 0.0642 nan 0.1000 -0.0002
## 480 0.0586 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0405
## 2 1.1632 nan 0.1000 0.0292
## 3 1.1057 nan 0.1000 0.0259
## 4 1.0539 nan 0.1000 0.0229
## 5 1.0083 nan 0.1000 0.0192
## 6 0.9683 nan 0.1000 0.0170
## 7 0.9354 nan 0.1000 0.0132
## 8 0.9041 nan 0.1000 0.0154
## 9 0.8761 nan 0.1000 0.0123
## 10 0.8519 nan 0.1000 0.0100
## 20 0.6943 nan 0.1000 0.0011
## 40 0.5614 nan 0.1000 -0.0005
## 60 0.4755 nan 0.1000 -0.0018
## 80 0.4244 nan 0.1000 -0.0008
## 100 0.3799 nan 0.1000 -0.0000
## 120 0.3345 nan 0.1000 -0.0005
## 140 0.3027 nan 0.1000 -0.0013
## 160 0.2761 nan 0.1000 -0.0000
## 180 0.2458 nan 0.1000 -0.0008
## 200 0.2218 nan 0.1000 -0.0006
## 220 0.1996 nan 0.1000 -0.0007
## 240 0.1822 nan 0.1000 -0.0009
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## 280 0.1493 nan 0.1000 -0.0006
## 300 0.1356 nan 0.1000 -0.0003
## 320 0.1239 nan 0.1000 -0.0002
## 340 0.1137 nan 0.1000 -0.0002
## 360 0.1027 nan 0.1000 -0.0002
## 380 0.0939 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0380
## 2 1.1674 nan 0.1000 0.0285
## 3 1.1020 nan 0.1000 0.0275
## 4 1.0506 nan 0.1000 0.0207
## 5 1.0083 nan 0.1000 0.0179
## 6 0.9731 nan 0.1000 0.0143
## 7 0.9379 nan 0.1000 0.0150
## 8 0.9058 nan 0.1000 0.0131
## 9 0.8767 nan 0.1000 0.0123
## 10 0.8506 nan 0.1000 0.0111
## 20 0.6959 nan 0.1000 0.0036
## 40 0.5707 nan 0.1000 0.0002
## 60 0.4848 nan 0.1000 -0.0022
## 80 0.4217 nan 0.1000 0.0001
## 100 0.3742 nan 0.1000 -0.0011
## 120 0.3345 nan 0.1000 -0.0008
## 140 0.2986 nan 0.1000 -0.0013
## 160 0.2694 nan 0.1000 -0.0007
## 180 0.2411 nan 0.1000 -0.0008
## 200 0.2169 nan 0.1000 -0.0005
## 220 0.1989 nan 0.1000 -0.0007
## 240 0.1795 nan 0.1000 -0.0005
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## 280 0.1512 nan 0.1000 -0.0004
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## 320 0.1281 nan 0.1000 -0.0002
## 340 0.1164 nan 0.1000 -0.0003
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## 380 0.0993 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2299 nan 0.1000 0.0396
## 2 1.1525 nan 0.1000 0.0348
## 3 1.0850 nan 0.1000 0.0300
## 4 1.0360 nan 0.1000 0.0209
## 5 0.9886 nan 0.1000 0.0198
## 6 0.9493 nan 0.1000 0.0165
## 7 0.9070 nan 0.1000 0.0177
## 8 0.8741 nan 0.1000 0.0122
## 9 0.8447 nan 0.1000 0.0123
## 10 0.8192 nan 0.1000 0.0107
## 20 0.6511 nan 0.1000 0.0004
## 40 0.5095 nan 0.1000 0.0002
## 60 0.4149 nan 0.1000 -0.0012
## 80 0.3518 nan 0.1000 -0.0002
## 100 0.2978 nan 0.1000 -0.0001
## 120 0.2555 nan 0.1000 -0.0007
## 140 0.2213 nan 0.1000 -0.0004
## 160 0.1963 nan 0.1000 -0.0006
## 180 0.1716 nan 0.1000 -0.0005
## 200 0.1509 nan 0.1000 -0.0003
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## 280 0.0931 nan 0.1000 -0.0001
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## 320 0.0762 nan 0.1000 -0.0001
## 340 0.0689 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2326 nan 0.1000 0.0396
## 2 1.1628 nan 0.1000 0.0312
## 3 1.0985 nan 0.1000 0.0313
## 4 1.0416 nan 0.1000 0.0242
## 5 0.9933 nan 0.1000 0.0188
## 6 0.9508 nan 0.1000 0.0144
## 7 0.9147 nan 0.1000 0.0153
## 8 0.8787 nan 0.1000 0.0119
## 9 0.8490 nan 0.1000 0.0124
## 10 0.8245 nan 0.1000 0.0092
## 20 0.6641 nan 0.1000 0.0010
## 40 0.5280 nan 0.1000 0.0003
## 60 0.4445 nan 0.1000 -0.0006
## 80 0.3750 nan 0.1000 -0.0005
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## 120 0.2829 nan 0.1000 -0.0014
## 140 0.2435 nan 0.1000 -0.0003
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## 180 0.1834 nan 0.1000 0.0002
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## 280 0.0978 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2302 nan 0.1000 0.0405
## 2 1.1556 nan 0.1000 0.0322
## 3 1.1020 nan 0.1000 0.0221
## 4 1.0469 nan 0.1000 0.0226
## 5 0.9985 nan 0.1000 0.0214
## 6 0.9571 nan 0.1000 0.0186
## 7 0.9200 nan 0.1000 0.0162
## 8 0.8891 nan 0.1000 0.0132
## 9 0.8587 nan 0.1000 0.0127
## 10 0.8375 nan 0.1000 0.0068
## 20 0.6701 nan 0.1000 0.0011
## 40 0.5279 nan 0.1000 -0.0005
## 60 0.4385 nan 0.1000 -0.0003
## 80 0.3770 nan 0.1000 -0.0006
## 100 0.3217 nan 0.1000 -0.0009
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## 140 0.2435 nan 0.1000 -0.0008
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## 180 0.1872 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2220 nan 0.1000 0.0449
## 2 1.1456 nan 0.1000 0.0328
## 3 1.0769 nan 0.1000 0.0298
## 4 1.0228 nan 0.1000 0.0230
## 5 0.9729 nan 0.1000 0.0230
## 6 0.9325 nan 0.1000 0.0163
## 7 0.8926 nan 0.1000 0.0167
## 8 0.8598 nan 0.1000 0.0101
## 9 0.8331 nan 0.1000 0.0109
## 10 0.8079 nan 0.1000 0.0086
## 20 0.6202 nan 0.1000 0.0021
## 40 0.4685 nan 0.1000 0.0001
## 60 0.3791 nan 0.1000 -0.0002
## 80 0.3083 nan 0.1000 -0.0005
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## 120 0.2179 nan 0.1000 -0.0008
## 140 0.1807 nan 0.1000 0.0001
## 160 0.1542 nan 0.1000 -0.0004
## 180 0.1333 nan 0.1000 -0.0005
## 200 0.1135 nan 0.1000 -0.0004
## 220 0.0971 nan 0.1000 -0.0002
## 240 0.0845 nan 0.1000 -0.0001
## 260 0.0732 nan 0.1000 -0.0000
## 280 0.0635 nan 0.1000 -0.0001
## 300 0.0547 nan 0.1000 -0.0000
## 320 0.0485 nan 0.1000 0.0000
## 340 0.0431 nan 0.1000 -0.0001
## 360 0.0377 nan 0.1000 -0.0001
## 380 0.0332 nan 0.1000 -0.0001
## 400 0.0294 nan 0.1000 -0.0001
## 420 0.0262 nan 0.1000 -0.0001
## 440 0.0236 nan 0.1000 -0.0001
## 460 0.0208 nan 0.1000 -0.0001
## 480 0.0186 nan 0.1000 -0.0001
## 500 0.0163 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2176 nan 0.1000 0.0449
## 2 1.1470 nan 0.1000 0.0321
## 3 1.0800 nan 0.1000 0.0275
## 4 1.0209 nan 0.1000 0.0240
## 5 0.9776 nan 0.1000 0.0180
## 6 0.9315 nan 0.1000 0.0182
## 7 0.8931 nan 0.1000 0.0151
## 8 0.8607 nan 0.1000 0.0114
## 9 0.8308 nan 0.1000 0.0123
## 10 0.8094 nan 0.1000 0.0081
## 20 0.6460 nan 0.1000 0.0017
## 40 0.4961 nan 0.1000 -0.0005
## 60 0.4043 nan 0.1000 -0.0017
## 80 0.3311 nan 0.1000 -0.0006
## 100 0.2779 nan 0.1000 -0.0003
## 120 0.2326 nan 0.1000 -0.0004
## 140 0.2003 nan 0.1000 -0.0004
## 160 0.1700 nan 0.1000 -0.0003
## 180 0.1423 nan 0.1000 -0.0003
## 200 0.1245 nan 0.1000 -0.0002
## 220 0.1074 nan 0.1000 -0.0001
## 240 0.0913 nan 0.1000 -0.0003
## 260 0.0798 nan 0.1000 -0.0005
## 280 0.0696 nan 0.1000 -0.0001
## 300 0.0601 nan 0.1000 -0.0001
## 320 0.0531 nan 0.1000 -0.0003
## 340 0.0465 nan 0.1000 -0.0003
## 360 0.0407 nan 0.1000 -0.0000
## 380 0.0359 nan 0.1000 -0.0001
## 400 0.0311 nan 0.1000 -0.0000
## 420 0.0278 nan 0.1000 -0.0001
## 440 0.0246 nan 0.1000 -0.0001
## 460 0.0215 nan 0.1000 -0.0000
## 480 0.0191 nan 0.1000 -0.0001
## 500 0.0167 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0394
## 2 1.1624 nan 0.1000 0.0359
## 3 1.0991 nan 0.1000 0.0315
## 4 1.0418 nan 0.1000 0.0226
## 5 0.9901 nan 0.1000 0.0228
## 6 0.9494 nan 0.1000 0.0177
## 7 0.9152 nan 0.1000 0.0138
## 8 0.8819 nan 0.1000 0.0136
## 9 0.8503 nan 0.1000 0.0113
## 10 0.8236 nan 0.1000 0.0109
## 20 0.6410 nan 0.1000 0.0018
## 40 0.4907 nan 0.1000 -0.0004
## 60 0.3960 nan 0.1000 -0.0019
## 80 0.3252 nan 0.1000 -0.0007
## 100 0.2745 nan 0.1000 -0.0008
## 120 0.2306 nan 0.1000 0.0002
## 140 0.1956 nan 0.1000 -0.0003
## 160 0.1687 nan 0.1000 -0.0002
## 180 0.1461 nan 0.1000 -0.0009
## 200 0.1275 nan 0.1000 -0.0009
## 220 0.1099 nan 0.1000 -0.0002
## 240 0.0967 nan 0.1000 -0.0006
## 260 0.0842 nan 0.1000 -0.0003
## 280 0.0726 nan 0.1000 -0.0003
## 300 0.0632 nan 0.1000 -0.0004
## 320 0.0555 nan 0.1000 -0.0000
## 340 0.0489 nan 0.1000 -0.0003
## 360 0.0429 nan 0.1000 -0.0002
## 380 0.0380 nan 0.1000 -0.0001
## 400 0.0332 nan 0.1000 -0.0002
## 420 0.0291 nan 0.1000 -0.0001
## 440 0.0258 nan 0.1000 -0.0002
## 460 0.0231 nan 0.1000 -0.0001
## 480 0.0206 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0005
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0003
## 60 1.2709 nan 0.0010 0.0003
## 80 1.2556 nan 0.0010 0.0004
## 100 1.2410 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2123 nan 0.0010 0.0003
## 160 1.1986 nan 0.0010 0.0003
## 180 1.1853 nan 0.0010 0.0003
## 200 1.1724 nan 0.0010 0.0003
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1475 nan 0.0010 0.0003
## 260 1.1353 nan 0.0010 0.0003
## 280 1.1237 nan 0.0010 0.0002
## 300 1.1124 nan 0.0010 0.0003
## 320 1.1013 nan 0.0010 0.0002
## 340 1.0908 nan 0.0010 0.0002
## 360 1.0806 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0607 nan 0.0010 0.0002
## 420 1.0512 nan 0.0010 0.0002
## 440 1.0422 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
## 500 1.0158 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2112 nan 0.0010 0.0003
## 160 1.1974 nan 0.0010 0.0003
## 180 1.1842 nan 0.0010 0.0003
## 200 1.1713 nan 0.0010 0.0003
## 220 1.1589 nan 0.0010 0.0003
## 240 1.1465 nan 0.0010 0.0003
## 260 1.1348 nan 0.0010 0.0003
## 280 1.1232 nan 0.0010 0.0003
## 300 1.1122 nan 0.0010 0.0002
## 320 1.1013 nan 0.0010 0.0002
## 340 1.0909 nan 0.0010 0.0002
## 360 1.0805 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0609 nan 0.0010 0.0002
## 420 1.0514 nan 0.0010 0.0002
## 440 1.0420 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
## 500 1.0159 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0003
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2712 nan 0.0010 0.0003
## 80 1.2559 nan 0.0010 0.0004
## 100 1.2409 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2124 nan 0.0010 0.0003
## 160 1.1989 nan 0.0010 0.0003
## 180 1.1858 nan 0.0010 0.0003
## 200 1.1728 nan 0.0010 0.0003
## 220 1.1602 nan 0.0010 0.0003
## 240 1.1481 nan 0.0010 0.0002
## 260 1.1366 nan 0.0010 0.0003
## 280 1.1252 nan 0.0010 0.0002
## 300 1.1141 nan 0.0010 0.0002
## 320 1.1033 nan 0.0010 0.0002
## 340 1.0927 nan 0.0010 0.0003
## 360 1.0826 nan 0.0010 0.0002
## 380 1.0724 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0533 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0351 nan 0.0010 0.0002
## 480 1.0266 nan 0.0010 0.0002
## 500 1.0180 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0005
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2844 nan 0.0010 0.0004
## 60 1.2671 nan 0.0010 0.0004
## 80 1.2505 nan 0.0010 0.0003
## 100 1.2348 nan 0.0010 0.0003
## 120 1.2192 nan 0.0010 0.0003
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1616 nan 0.0010 0.0003
## 220 1.1482 nan 0.0010 0.0003
## 240 1.1352 nan 0.0010 0.0003
## 260 1.1225 nan 0.0010 0.0003
## 280 1.1103 nan 0.0010 0.0003
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0872 nan 0.0010 0.0003
## 340 1.0760 nan 0.0010 0.0003
## 360 1.0650 nan 0.0010 0.0002
## 380 1.0545 nan 0.0010 0.0002
## 400 1.0442 nan 0.0010 0.0002
## 420 1.0344 nan 0.0010 0.0002
## 440 1.0244 nan 0.0010 0.0002
## 460 1.0147 nan 0.0010 0.0002
## 480 1.0054 nan 0.0010 0.0002
## 500 0.9964 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0003
## 60 1.2678 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0003
## 100 1.2352 nan 0.0010 0.0003
## 120 1.2192 nan 0.0010 0.0003
## 140 1.2042 nan 0.0010 0.0004
## 160 1.1898 nan 0.0010 0.0003
## 180 1.1758 nan 0.0010 0.0003
## 200 1.1619 nan 0.0010 0.0003
## 220 1.1487 nan 0.0010 0.0003
## 240 1.1357 nan 0.0010 0.0003
## 260 1.1232 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0994 nan 0.0010 0.0002
## 320 1.0880 nan 0.0010 0.0003
## 340 1.0769 nan 0.0010 0.0002
## 360 1.0660 nan 0.0010 0.0002
## 380 1.0556 nan 0.0010 0.0003
## 400 1.0453 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0254 nan 0.0010 0.0002
## 460 1.0159 nan 0.0010 0.0002
## 480 1.0065 nan 0.0010 0.0002
## 500 0.9976 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0005
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2516 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2200 nan 0.0010 0.0003
## 140 1.2050 nan 0.0010 0.0003
## 160 1.1905 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
## 200 1.1632 nan 0.0010 0.0003
## 220 1.1500 nan 0.0010 0.0003
## 240 1.1368 nan 0.0010 0.0003
## 260 1.1246 nan 0.0010 0.0003
## 280 1.1125 nan 0.0010 0.0002
## 300 1.1010 nan 0.0010 0.0002
## 320 1.0898 nan 0.0010 0.0003
## 340 1.0790 nan 0.0010 0.0002
## 360 1.0682 nan 0.0010 0.0002
## 380 1.0578 nan 0.0010 0.0002
## 400 1.0479 nan 0.0010 0.0002
## 420 1.0381 nan 0.0010 0.0002
## 440 1.0284 nan 0.0010 0.0002
## 460 1.0191 nan 0.0010 0.0002
## 480 1.0102 nan 0.0010 0.0002
## 500 1.0012 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2648 nan 0.0010 0.0004
## 80 1.2474 nan 0.0010 0.0004
## 100 1.2304 nan 0.0010 0.0004
## 120 1.2138 nan 0.0010 0.0004
## 140 1.1979 nan 0.0010 0.0004
## 160 1.1823 nan 0.0010 0.0004
## 180 1.1677 nan 0.0010 0.0003
## 200 1.1531 nan 0.0010 0.0003
## 220 1.1393 nan 0.0010 0.0003
## 240 1.1260 nan 0.0010 0.0003
## 260 1.1130 nan 0.0010 0.0003
## 280 1.1001 nan 0.0010 0.0003
## 300 1.0877 nan 0.0010 0.0003
## 320 1.0756 nan 0.0010 0.0002
## 340 1.0640 nan 0.0010 0.0003
## 360 1.0523 nan 0.0010 0.0003
## 380 1.0413 nan 0.0010 0.0002
## 400 1.0304 nan 0.0010 0.0002
## 420 1.0199 nan 0.0010 0.0002
## 440 1.0098 nan 0.0010 0.0002
## 460 0.9997 nan 0.0010 0.0002
## 480 0.9898 nan 0.0010 0.0002
## 500 0.9800 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2478 nan 0.0010 0.0003
## 100 1.2309 nan 0.0010 0.0004
## 120 1.2148 nan 0.0010 0.0003
## 140 1.1993 nan 0.0010 0.0003
## 160 1.1836 nan 0.0010 0.0003
## 180 1.1690 nan 0.0010 0.0003
## 200 1.1548 nan 0.0010 0.0003
## 220 1.1411 nan 0.0010 0.0003
## 240 1.1276 nan 0.0010 0.0003
## 260 1.1147 nan 0.0010 0.0003
## 280 1.1021 nan 0.0010 0.0003
## 300 1.0896 nan 0.0010 0.0003
## 320 1.0772 nan 0.0010 0.0002
## 340 1.0656 nan 0.0010 0.0002
## 360 1.0544 nan 0.0010 0.0002
## 380 1.0434 nan 0.0010 0.0002
## 400 1.0328 nan 0.0010 0.0002
## 420 1.0226 nan 0.0010 0.0002
## 440 1.0123 nan 0.0010 0.0002
## 460 1.0024 nan 0.0010 0.0002
## 480 0.9927 nan 0.0010 0.0002
## 500 0.9832 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0005
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2657 nan 0.0010 0.0004
## 80 1.2484 nan 0.0010 0.0004
## 100 1.2321 nan 0.0010 0.0004
## 120 1.2161 nan 0.0010 0.0004
## 140 1.2007 nan 0.0010 0.0003
## 160 1.1858 nan 0.0010 0.0003
## 180 1.1711 nan 0.0010 0.0003
## 200 1.1572 nan 0.0010 0.0003
## 220 1.1435 nan 0.0010 0.0003
## 240 1.1303 nan 0.0010 0.0003
## 260 1.1174 nan 0.0010 0.0003
## 280 1.1051 nan 0.0010 0.0002
## 300 1.0930 nan 0.0010 0.0003
## 320 1.0814 nan 0.0010 0.0002
## 340 1.0697 nan 0.0010 0.0003
## 360 1.0586 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0002
## 400 1.0373 nan 0.0010 0.0002
## 420 1.0274 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0075 nan 0.0010 0.0002
## 480 0.9981 nan 0.0010 0.0002
## 500 0.9890 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0035
## 2 1.3041 nan 0.0100 0.0040
## 3 1.2960 nan 0.0100 0.0035
## 4 1.2883 nan 0.0100 0.0031
## 5 1.2802 nan 0.0100 0.0038
## 6 1.2715 nan 0.0100 0.0035
## 7 1.2636 nan 0.0100 0.0035
## 8 1.2552 nan 0.0100 0.0035
## 9 1.2476 nan 0.0100 0.0033
## 10 1.2399 nan 0.0100 0.0037
## 20 1.1685 nan 0.0100 0.0031
## 40 1.0582 nan 0.0100 0.0024
## 60 0.9744 nan 0.0100 0.0016
## 80 0.9076 nan 0.0100 0.0011
## 100 0.8542 nan 0.0100 0.0010
## 120 0.8104 nan 0.0100 0.0006
## 140 0.7743 nan 0.0100 0.0006
## 160 0.7446 nan 0.0100 0.0004
## 180 0.7176 nan 0.0100 0.0002
## 200 0.6940 nan 0.0100 0.0003
## 220 0.6731 nan 0.0100 0.0003
## 240 0.6559 nan 0.0100 0.0002
## 260 0.6391 nan 0.0100 0.0002
## 280 0.6233 nan 0.0100 0.0000
## 300 0.6093 nan 0.0100 0.0000
## 320 0.5974 nan 0.0100 0.0002
## 340 0.5841 nan 0.0100 0.0001
## 360 0.5734 nan 0.0100 0.0002
## 380 0.5624 nan 0.0100 -0.0001
## 400 0.5524 nan 0.0100 -0.0001
## 420 0.5432 nan 0.0100 -0.0002
## 440 0.5348 nan 0.0100 -0.0000
## 460 0.5267 nan 0.0100 -0.0000
## 480 0.5180 nan 0.0100 -0.0000
## 500 0.5097 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3035 nan 0.0100 0.0039
## 3 1.2949 nan 0.0100 0.0039
## 4 1.2860 nan 0.0100 0.0040
## 5 1.2791 nan 0.0100 0.0034
## 6 1.2707 nan 0.0100 0.0037
## 7 1.2632 nan 0.0100 0.0035
## 8 1.2561 nan 0.0100 0.0031
## 9 1.2492 nan 0.0100 0.0032
## 10 1.2419 nan 0.0100 0.0034
## 20 1.1738 nan 0.0100 0.0030
## 40 1.0623 nan 0.0100 0.0021
## 60 0.9771 nan 0.0100 0.0018
## 80 0.9114 nan 0.0100 0.0011
## 100 0.8583 nan 0.0100 0.0009
## 120 0.8139 nan 0.0100 0.0006
## 140 0.7772 nan 0.0100 0.0006
## 160 0.7470 nan 0.0100 0.0003
## 180 0.7216 nan 0.0100 0.0004
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## 220 0.6792 nan 0.0100 0.0004
## 240 0.6609 nan 0.0100 0.0002
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## 280 0.6304 nan 0.0100 -0.0001
## 300 0.6169 nan 0.0100 0.0001
## 320 0.6033 nan 0.0100 0.0001
## 340 0.5910 nan 0.0100 0.0000
## 360 0.5800 nan 0.0100 -0.0001
## 380 0.5697 nan 0.0100 -0.0000
## 400 0.5594 nan 0.0100 -0.0001
## 420 0.5498 nan 0.0100 -0.0001
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## 460 0.5307 nan 0.0100 0.0001
## 480 0.5226 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0037
## 3 1.2947 nan 0.0100 0.0042
## 4 1.2870 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0036
## 6 1.2711 nan 0.0100 0.0032
## 7 1.2635 nan 0.0100 0.0034
## 8 1.2552 nan 0.0100 0.0040
## 9 1.2482 nan 0.0100 0.0031
## 10 1.2406 nan 0.0100 0.0035
## 20 1.1719 nan 0.0100 0.0026
## 40 1.0614 nan 0.0100 0.0019
## 60 0.9790 nan 0.0100 0.0013
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## 100 0.8585 nan 0.0100 0.0009
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## 240 0.6631 nan 0.0100 0.0002
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## 300 0.6198 nan 0.0100 0.0000
## 320 0.6089 nan 0.0100 0.0001
## 340 0.5970 nan 0.0100 -0.0002
## 360 0.5860 nan 0.0100 0.0001
## 380 0.5754 nan 0.0100 -0.0001
## 400 0.5654 nan 0.0100 -0.0000
## 420 0.5562 nan 0.0100 -0.0001
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## 460 0.5382 nan 0.0100 -0.0002
## 480 0.5301 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0038
## 2 1.3033 nan 0.0100 0.0040
## 3 1.2943 nan 0.0100 0.0040
## 4 1.2859 nan 0.0100 0.0031
## 5 1.2770 nan 0.0100 0.0037
## 6 1.2688 nan 0.0100 0.0035
## 7 1.2599 nan 0.0100 0.0037
## 8 1.2526 nan 0.0100 0.0034
## 9 1.2444 nan 0.0100 0.0034
## 10 1.2364 nan 0.0100 0.0032
## 20 1.1644 nan 0.0100 0.0032
## 40 1.0444 nan 0.0100 0.0021
## 60 0.9515 nan 0.0100 0.0019
## 80 0.8801 nan 0.0100 0.0013
## 100 0.8239 nan 0.0100 0.0010
## 120 0.7780 nan 0.0100 0.0008
## 140 0.7400 nan 0.0100 0.0004
## 160 0.7080 nan 0.0100 0.0004
## 180 0.6802 nan 0.0100 0.0003
## 200 0.6561 nan 0.0100 0.0001
## 220 0.6334 nan 0.0100 0.0002
## 240 0.6133 nan 0.0100 -0.0000
## 260 0.5954 nan 0.0100 0.0001
## 280 0.5805 nan 0.0100 0.0001
## 300 0.5648 nan 0.0100 0.0001
## 320 0.5510 nan 0.0100 0.0001
## 340 0.5388 nan 0.0100 -0.0001
## 360 0.5264 nan 0.0100 0.0001
## 380 0.5155 nan 0.0100 0.0001
## 400 0.5043 nan 0.0100 0.0000
## 420 0.4933 nan 0.0100 0.0002
## 440 0.4836 nan 0.0100 -0.0000
## 460 0.4739 nan 0.0100 0.0001
## 480 0.4654 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3023 nan 0.0100 0.0035
## 3 1.2943 nan 0.0100 0.0034
## 4 1.2859 nan 0.0100 0.0037
## 5 1.2772 nan 0.0100 0.0038
## 6 1.2683 nan 0.0100 0.0041
## 7 1.2595 nan 0.0100 0.0036
## 8 1.2515 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0037
## 10 1.2348 nan 0.0100 0.0035
## 20 1.1624 nan 0.0100 0.0026
## 40 1.0462 nan 0.0100 0.0023
## 60 0.9555 nan 0.0100 0.0017
## 80 0.8847 nan 0.0100 0.0014
## 100 0.8285 nan 0.0100 0.0011
## 120 0.7841 nan 0.0100 0.0008
## 140 0.7476 nan 0.0100 0.0007
## 160 0.7164 nan 0.0100 0.0004
## 180 0.6886 nan 0.0100 0.0003
## 200 0.6638 nan 0.0100 0.0003
## 220 0.6414 nan 0.0100 0.0003
## 240 0.6212 nan 0.0100 0.0002
## 260 0.6050 nan 0.0100 0.0002
## 280 0.5893 nan 0.0100 0.0001
## 300 0.5746 nan 0.0100 0.0000
## 320 0.5615 nan 0.0100 0.0000
## 340 0.5485 nan 0.0100 0.0000
## 360 0.5356 nan 0.0100 -0.0001
## 380 0.5249 nan 0.0100 0.0001
## 400 0.5139 nan 0.0100 -0.0002
## 420 0.5031 nan 0.0100 -0.0000
## 440 0.4929 nan 0.0100 -0.0001
## 460 0.4836 nan 0.0100 -0.0000
## 480 0.4744 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2942 nan 0.0100 0.0044
## 4 1.2859 nan 0.0100 0.0036
## 5 1.2773 nan 0.0100 0.0035
## 6 1.2691 nan 0.0100 0.0036
## 7 1.2608 nan 0.0100 0.0041
## 8 1.2528 nan 0.0100 0.0036
## 9 1.2449 nan 0.0100 0.0038
## 10 1.2370 nan 0.0100 0.0031
## 20 1.1634 nan 0.0100 0.0032
## 40 1.0476 nan 0.0100 0.0021
## 60 0.9584 nan 0.0100 0.0015
## 80 0.8901 nan 0.0100 0.0013
## 100 0.8341 nan 0.0100 0.0012
## 120 0.7869 nan 0.0100 0.0009
## 140 0.7485 nan 0.0100 0.0004
## 160 0.7172 nan 0.0100 0.0004
## 180 0.6896 nan 0.0100 0.0002
## 200 0.6655 nan 0.0100 0.0002
## 220 0.6438 nan 0.0100 0.0001
## 240 0.6256 nan 0.0100 0.0003
## 260 0.6092 nan 0.0100 0.0000
## 280 0.5937 nan 0.0100 0.0000
## 300 0.5797 nan 0.0100 0.0000
## 320 0.5653 nan 0.0100 0.0001
## 340 0.5520 nan 0.0100 0.0000
## 360 0.5398 nan 0.0100 -0.0001
## 380 0.5291 nan 0.0100 0.0000
## 400 0.5179 nan 0.0100 -0.0000
## 420 0.5075 nan 0.0100 0.0000
## 440 0.4985 nan 0.0100 -0.0000
## 460 0.4893 nan 0.0100 0.0001
## 480 0.4798 nan 0.0100 -0.0000
## 500 0.4713 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0040
## 2 1.3024 nan 0.0100 0.0039
## 3 1.2933 nan 0.0100 0.0040
## 4 1.2835 nan 0.0100 0.0046
## 5 1.2744 nan 0.0100 0.0043
## 6 1.2657 nan 0.0100 0.0040
## 7 1.2564 nan 0.0100 0.0043
## 8 1.2478 nan 0.0100 0.0040
## 9 1.2388 nan 0.0100 0.0040
## 10 1.2307 nan 0.0100 0.0033
## 20 1.1550 nan 0.0100 0.0028
## 40 1.0318 nan 0.0100 0.0024
## 60 0.9370 nan 0.0100 0.0018
## 80 0.8632 nan 0.0100 0.0014
## 100 0.8060 nan 0.0100 0.0006
## 120 0.7585 nan 0.0100 0.0006
## 140 0.7185 nan 0.0100 0.0005
## 160 0.6860 nan 0.0100 0.0005
## 180 0.6549 nan 0.0100 0.0007
## 200 0.6284 nan 0.0100 0.0002
## 220 0.6048 nan 0.0100 0.0002
## 240 0.5850 nan 0.0100 0.0000
## 260 0.5662 nan 0.0100 0.0001
## 280 0.5498 nan 0.0100 0.0000
## 300 0.5340 nan 0.0100 0.0001
## 320 0.5185 nan 0.0100 0.0001
## 340 0.5039 nan 0.0100 -0.0000
## 360 0.4912 nan 0.0100 0.0000
## 380 0.4786 nan 0.0100 -0.0001
## 400 0.4659 nan 0.0100 -0.0000
## 420 0.4546 nan 0.0100 -0.0001
## 440 0.4435 nan 0.0100 0.0001
## 460 0.4334 nan 0.0100 0.0001
## 480 0.4244 nan 0.0100 -0.0000
## 500 0.4144 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3019 nan 0.0100 0.0047
## 3 1.2910 nan 0.0100 0.0048
## 4 1.2805 nan 0.0100 0.0047
## 5 1.2723 nan 0.0100 0.0040
## 6 1.2631 nan 0.0100 0.0043
## 7 1.2537 nan 0.0100 0.0042
## 8 1.2445 nan 0.0100 0.0039
## 9 1.2364 nan 0.0100 0.0040
## 10 1.2288 nan 0.0100 0.0031
## 20 1.1542 nan 0.0100 0.0031
## 40 1.0337 nan 0.0100 0.0027
## 60 0.9395 nan 0.0100 0.0017
## 80 0.8667 nan 0.0100 0.0014
## 100 0.8064 nan 0.0100 0.0013
## 120 0.7595 nan 0.0100 0.0006
## 140 0.7220 nan 0.0100 0.0005
## 160 0.6886 nan 0.0100 0.0003
## 180 0.6611 nan 0.0100 0.0001
## 200 0.6346 nan 0.0100 0.0004
## 220 0.6110 nan 0.0100 0.0001
## 240 0.5898 nan 0.0100 0.0004
## 260 0.5711 nan 0.0100 0.0002
## 280 0.5545 nan 0.0100 0.0002
## 300 0.5386 nan 0.0100 0.0000
## 320 0.5245 nan 0.0100 0.0001
## 340 0.5109 nan 0.0100 -0.0001
## 360 0.4976 nan 0.0100 -0.0001
## 380 0.4860 nan 0.0100 0.0000
## 400 0.4746 nan 0.0100 0.0001
## 420 0.4636 nan 0.0100 0.0000
## 440 0.4527 nan 0.0100 0.0001
## 460 0.4419 nan 0.0100 -0.0001
## 480 0.4326 nan 0.0100 -0.0001
## 500 0.4233 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3099 nan 0.0100 0.0047
## 2 1.3005 nan 0.0100 0.0045
## 3 1.2915 nan 0.0100 0.0043
## 4 1.2828 nan 0.0100 0.0037
## 5 1.2735 nan 0.0100 0.0044
## 6 1.2647 nan 0.0100 0.0041
## 7 1.2567 nan 0.0100 0.0032
## 8 1.2482 nan 0.0100 0.0038
## 9 1.2393 nan 0.0100 0.0041
## 10 1.2317 nan 0.0100 0.0035
## 20 1.1549 nan 0.0100 0.0037
## 40 1.0338 nan 0.0100 0.0027
## 60 0.9418 nan 0.0100 0.0018
## 80 0.8711 nan 0.0100 0.0012
## 100 0.8134 nan 0.0100 0.0011
## 120 0.7669 nan 0.0100 0.0006
## 140 0.7268 nan 0.0100 0.0007
## 160 0.6945 nan 0.0100 0.0002
## 180 0.6654 nan 0.0100 0.0003
## 200 0.6415 nan 0.0100 -0.0001
## 220 0.6183 nan 0.0100 0.0002
## 240 0.5986 nan 0.0100 0.0000
## 260 0.5795 nan 0.0100 0.0000
## 280 0.5619 nan 0.0100 0.0002
## 300 0.5464 nan 0.0100 0.0000
## 320 0.5327 nan 0.0100 0.0001
## 340 0.5201 nan 0.0100 -0.0001
## 360 0.5077 nan 0.0100 -0.0003
## 380 0.4950 nan 0.0100 0.0000
## 400 0.4835 nan 0.0100 -0.0001
## 420 0.4721 nan 0.0100 -0.0001
## 440 0.4614 nan 0.0100 0.0000
## 460 0.4519 nan 0.0100 -0.0000
## 480 0.4422 nan 0.0100 0.0001
## 500 0.4331 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0362
## 2 1.1710 nan 0.1000 0.0260
## 3 1.1130 nan 0.1000 0.0255
## 4 1.0554 nan 0.1000 0.0243
## 5 1.0034 nan 0.1000 0.0214
## 6 0.9617 nan 0.1000 0.0186
## 7 0.9263 nan 0.1000 0.0130
## 8 0.8922 nan 0.1000 0.0150
## 9 0.8658 nan 0.1000 0.0113
## 10 0.8426 nan 0.1000 0.0087
## 20 0.6823 nan 0.1000 0.0042
## 40 0.5503 nan 0.1000 -0.0005
## 60 0.4665 nan 0.1000 0.0003
## 80 0.4030 nan 0.1000 -0.0005
## 100 0.3523 nan 0.1000 -0.0002
## 120 0.3096 nan 0.1000 -0.0001
## 140 0.2773 nan 0.1000 -0.0003
## 160 0.2490 nan 0.1000 -0.0005
## 180 0.2282 nan 0.1000 -0.0002
## 200 0.2048 nan 0.1000 -0.0008
## 220 0.1834 nan 0.1000 -0.0004
## 240 0.1651 nan 0.1000 -0.0002
## 260 0.1507 nan 0.1000 -0.0003
## 280 0.1360 nan 0.1000 -0.0006
## 300 0.1234 nan 0.1000 -0.0000
## 320 0.1124 nan 0.1000 -0.0001
## 340 0.1035 nan 0.1000 -0.0002
## 360 0.0952 nan 0.1000 -0.0002
## 380 0.0872 nan 0.1000 -0.0003
## 400 0.0799 nan 0.1000 -0.0003
## 420 0.0731 nan 0.1000 -0.0005
## 440 0.0666 nan 0.1000 -0.0001
## 460 0.0617 nan 0.1000 -0.0001
## 480 0.0563 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0415
## 2 1.1683 nan 0.1000 0.0301
## 3 1.1056 nan 0.1000 0.0283
## 4 1.0535 nan 0.1000 0.0223
## 5 1.0095 nan 0.1000 0.0197
## 6 0.9746 nan 0.1000 0.0136
## 7 0.9401 nan 0.1000 0.0132
## 8 0.9106 nan 0.1000 0.0123
## 9 0.8821 nan 0.1000 0.0104
## 10 0.8532 nan 0.1000 0.0111
## 20 0.6928 nan 0.1000 0.0019
## 40 0.5538 nan 0.1000 -0.0002
## 60 0.4785 nan 0.1000 -0.0003
## 80 0.4160 nan 0.1000 -0.0000
## 100 0.3627 nan 0.1000 -0.0002
## 120 0.3154 nan 0.1000 -0.0001
## 140 0.2827 nan 0.1000 -0.0006
## 160 0.2513 nan 0.1000 -0.0002
## 180 0.2274 nan 0.1000 -0.0004
## 200 0.2078 nan 0.1000 0.0000
## 220 0.1901 nan 0.1000 -0.0002
## 240 0.1726 nan 0.1000 -0.0007
## 260 0.1559 nan 0.1000 -0.0004
## 280 0.1433 nan 0.1000 -0.0006
## 300 0.1309 nan 0.1000 -0.0006
## 320 0.1189 nan 0.1000 -0.0001
## 340 0.1096 nan 0.1000 -0.0002
## 360 0.1010 nan 0.1000 -0.0005
## 380 0.0922 nan 0.1000 -0.0003
## 400 0.0844 nan 0.1000 -0.0003
## 420 0.0779 nan 0.1000 -0.0002
## 440 0.0714 nan 0.1000 -0.0003
## 460 0.0661 nan 0.1000 -0.0001
## 480 0.0609 nan 0.1000 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2340 nan 0.1000 0.0385
## 2 1.1709 nan 0.1000 0.0248
## 3 1.1110 nan 0.1000 0.0240
## 4 1.0545 nan 0.1000 0.0271
## 5 1.0102 nan 0.1000 0.0194
## 6 0.9686 nan 0.1000 0.0198
## 7 0.9356 nan 0.1000 0.0119
## 8 0.9073 nan 0.1000 0.0133
## 9 0.8794 nan 0.1000 0.0123
## 10 0.8561 nan 0.1000 0.0103
## 20 0.6948 nan 0.1000 0.0028
## 40 0.5612 nan 0.1000 -0.0014
## 60 0.4826 nan 0.1000 0.0001
## 80 0.4252 nan 0.1000 0.0009
## 100 0.3733 nan 0.1000 0.0004
## 120 0.3315 nan 0.1000 -0.0020
## 140 0.3007 nan 0.1000 -0.0008
## 160 0.2701 nan 0.1000 -0.0010
## 180 0.2413 nan 0.1000 -0.0006
## 200 0.2201 nan 0.1000 -0.0011
## 220 0.1977 nan 0.1000 -0.0010
## 240 0.1781 nan 0.1000 -0.0002
## 260 0.1608 nan 0.1000 -0.0008
## 280 0.1489 nan 0.1000 -0.0003
## 300 0.1367 nan 0.1000 -0.0006
## 320 0.1239 nan 0.1000 -0.0007
## 340 0.1144 nan 0.1000 -0.0003
## 360 0.1074 nan 0.1000 -0.0004
## 380 0.0989 nan 0.1000 -0.0004
## 400 0.0919 nan 0.1000 -0.0005
## 420 0.0840 nan 0.1000 -0.0001
## 440 0.0776 nan 0.1000 -0.0003
## 460 0.0715 nan 0.1000 -0.0002
## 480 0.0658 nan 0.1000 -0.0002
## 500 0.0606 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0416
## 2 1.1550 nan 0.1000 0.0317
## 3 1.0948 nan 0.1000 0.0243
## 4 1.0397 nan 0.1000 0.0238
## 5 0.9916 nan 0.1000 0.0188
## 6 0.9509 nan 0.1000 0.0197
## 7 0.9172 nan 0.1000 0.0111
## 8 0.8822 nan 0.1000 0.0146
## 9 0.8548 nan 0.1000 0.0095
## 10 0.8260 nan 0.1000 0.0107
## 20 0.6525 nan 0.1000 0.0026
## 40 0.5086 nan 0.1000 -0.0007
## 60 0.4250 nan 0.1000 -0.0004
## 80 0.3579 nan 0.1000 -0.0007
## 100 0.3044 nan 0.1000 0.0001
## 120 0.2673 nan 0.1000 -0.0001
## 140 0.2299 nan 0.1000 -0.0007
## 160 0.1999 nan 0.1000 -0.0004
## 180 0.1775 nan 0.1000 -0.0007
## 200 0.1541 nan 0.1000 -0.0004
## 220 0.1358 nan 0.1000 -0.0001
## 240 0.1197 nan 0.1000 -0.0005
## 260 0.1076 nan 0.1000 0.0000
## 280 0.0963 nan 0.1000 -0.0001
## 300 0.0860 nan 0.1000 -0.0001
## 320 0.0765 nan 0.1000 -0.0003
## 340 0.0697 nan 0.1000 -0.0002
## 360 0.0629 nan 0.1000 -0.0001
## 380 0.0569 nan 0.1000 -0.0001
## 400 0.0514 nan 0.1000 -0.0001
## 420 0.0463 nan 0.1000 -0.0001
## 440 0.0417 nan 0.1000 -0.0003
## 460 0.0377 nan 0.1000 -0.0001
## 480 0.0340 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2319 nan 0.1000 0.0388
## 2 1.1571 nan 0.1000 0.0308
## 3 1.0934 nan 0.1000 0.0265
## 4 1.0392 nan 0.1000 0.0233
## 5 0.9942 nan 0.1000 0.0203
## 6 0.9508 nan 0.1000 0.0175
## 7 0.9136 nan 0.1000 0.0163
## 8 0.8850 nan 0.1000 0.0122
## 9 0.8568 nan 0.1000 0.0109
## 10 0.8311 nan 0.1000 0.0070
## 20 0.6659 nan 0.1000 0.0046
## 40 0.5144 nan 0.1000 0.0007
## 60 0.4251 nan 0.1000 0.0000
## 80 0.3657 nan 0.1000 -0.0014
## 100 0.3116 nan 0.1000 -0.0014
## 120 0.2711 nan 0.1000 -0.0004
## 140 0.2349 nan 0.1000 -0.0008
## 160 0.2067 nan 0.1000 -0.0003
## 180 0.1812 nan 0.1000 -0.0007
## 200 0.1579 nan 0.1000 -0.0004
## 220 0.1392 nan 0.1000 -0.0004
## 240 0.1228 nan 0.1000 -0.0005
## 260 0.1092 nan 0.1000 -0.0005
## 280 0.0964 nan 0.1000 -0.0002
## 300 0.0872 nan 0.1000 -0.0002
## 320 0.0787 nan 0.1000 -0.0002
## 340 0.0696 nan 0.1000 0.0000
## 360 0.0626 nan 0.1000 -0.0002
## 380 0.0567 nan 0.1000 -0.0003
## 400 0.0507 nan 0.1000 -0.0001
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## 440 0.0416 nan 0.1000 -0.0001
## 460 0.0378 nan 0.1000 -0.0002
## 480 0.0340 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2418 nan 0.1000 0.0333
## 2 1.1624 nan 0.1000 0.0365
## 3 1.0999 nan 0.1000 0.0283
## 4 1.0443 nan 0.1000 0.0210
## 5 0.9975 nan 0.1000 0.0207
## 6 0.9558 nan 0.1000 0.0175
## 7 0.9186 nan 0.1000 0.0146
## 8 0.8840 nan 0.1000 0.0124
## 9 0.8533 nan 0.1000 0.0111
## 10 0.8218 nan 0.1000 0.0128
## 20 0.6663 nan 0.1000 0.0028
## 40 0.5271 nan 0.1000 -0.0000
## 60 0.4425 nan 0.1000 -0.0004
## 80 0.3795 nan 0.1000 -0.0011
## 100 0.3254 nan 0.1000 -0.0006
## 120 0.2800 nan 0.1000 -0.0010
## 140 0.2450 nan 0.1000 -0.0007
## 160 0.2099 nan 0.1000 -0.0006
## 180 0.1836 nan 0.1000 -0.0001
## 200 0.1603 nan 0.1000 -0.0008
## 220 0.1423 nan 0.1000 -0.0003
## 240 0.1259 nan 0.1000 -0.0005
## 260 0.1130 nan 0.1000 -0.0006
## 280 0.1017 nan 0.1000 -0.0003
## 300 0.0913 nan 0.1000 -0.0002
## 320 0.0823 nan 0.1000 -0.0008
## 340 0.0742 nan 0.1000 -0.0000
## 360 0.0659 nan 0.1000 -0.0002
## 380 0.0594 nan 0.1000 -0.0001
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## 460 0.0397 nan 0.1000 -0.0002
## 480 0.0361 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0370
## 2 1.1564 nan 0.1000 0.0347
## 3 1.0940 nan 0.1000 0.0257
## 4 1.0399 nan 0.1000 0.0230
## 5 0.9841 nan 0.1000 0.0225
## 6 0.9410 nan 0.1000 0.0173
## 7 0.9031 nan 0.1000 0.0162
## 8 0.8645 nan 0.1000 0.0164
## 9 0.8330 nan 0.1000 0.0141
## 10 0.8044 nan 0.1000 0.0103
## 20 0.6343 nan 0.1000 0.0035
## 40 0.4703 nan 0.1000 -0.0001
## 60 0.3712 nan 0.1000 -0.0002
## 80 0.3094 nan 0.1000 -0.0010
## 100 0.2570 nan 0.1000 -0.0003
## 120 0.2136 nan 0.1000 -0.0006
## 140 0.1824 nan 0.1000 -0.0004
## 160 0.1549 nan 0.1000 -0.0005
## 180 0.1319 nan 0.1000 0.0002
## 200 0.1128 nan 0.1000 -0.0003
## 220 0.0987 nan 0.1000 -0.0003
## 240 0.0855 nan 0.1000 -0.0002
## 260 0.0743 nan 0.1000 -0.0001
## 280 0.0650 nan 0.1000 -0.0003
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## 320 0.0494 nan 0.1000 -0.0000
## 340 0.0439 nan 0.1000 -0.0001
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## 400 0.0304 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2251 nan 0.1000 0.0421
## 2 1.1527 nan 0.1000 0.0330
## 3 1.0842 nan 0.1000 0.0295
## 4 1.0286 nan 0.1000 0.0230
## 5 0.9785 nan 0.1000 0.0200
## 6 0.9341 nan 0.1000 0.0174
## 7 0.8966 nan 0.1000 0.0144
## 8 0.8612 nan 0.1000 0.0119
## 9 0.8346 nan 0.1000 0.0089
## 10 0.8122 nan 0.1000 0.0099
## 20 0.6520 nan 0.1000 0.0021
## 40 0.4859 nan 0.1000 -0.0008
## 60 0.3838 nan 0.1000 -0.0001
## 80 0.3218 nan 0.1000 -0.0014
## 100 0.2645 nan 0.1000 0.0004
## 120 0.2221 nan 0.1000 -0.0006
## 140 0.1859 nan 0.1000 -0.0016
## 160 0.1596 nan 0.1000 -0.0006
## 180 0.1362 nan 0.1000 -0.0003
## 200 0.1172 nan 0.1000 -0.0005
## 220 0.1018 nan 0.1000 -0.0003
## 240 0.0885 nan 0.1000 -0.0003
## 260 0.0766 nan 0.1000 -0.0004
## 280 0.0671 nan 0.1000 -0.0001
## 300 0.0587 nan 0.1000 -0.0001
## 320 0.0517 nan 0.1000 -0.0002
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## 380 0.0358 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2203 nan 0.1000 0.0452
## 2 1.1399 nan 0.1000 0.0359
## 3 1.0795 nan 0.1000 0.0277
## 4 1.0241 nan 0.1000 0.0238
## 5 0.9755 nan 0.1000 0.0216
## 6 0.9349 nan 0.1000 0.0168
## 7 0.9006 nan 0.1000 0.0127
## 8 0.8702 nan 0.1000 0.0119
## 9 0.8371 nan 0.1000 0.0120
## 10 0.8123 nan 0.1000 0.0084
## 20 0.6361 nan 0.1000 0.0026
## 40 0.4837 nan 0.1000 0.0016
## 60 0.3914 nan 0.1000 -0.0006
## 80 0.3241 nan 0.1000 -0.0000
## 100 0.2706 nan 0.1000 -0.0007
## 120 0.2256 nan 0.1000 -0.0001
## 140 0.1925 nan 0.1000 -0.0006
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## 180 0.1423 nan 0.1000 -0.0005
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## 220 0.1053 nan 0.1000 -0.0007
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## 280 0.0703 nan 0.1000 -0.0003
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## 400 0.0336 nan 0.1000 -0.0001
## 420 0.0299 nan 0.1000 -0.0001
## 440 0.0265 nan 0.1000 -0.0001
## 460 0.0235 nan 0.1000 -0.0001
## 480 0.0210 nan 0.1000 -0.0001
## 500 0.0186 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2584 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2303 nan 0.0010 0.0003
## 140 1.2168 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1912 nan 0.0010 0.0003
## 200 1.1790 nan 0.0010 0.0002
## 220 1.1672 nan 0.0010 0.0003
## 240 1.1558 nan 0.0010 0.0003
## 260 1.1446 nan 0.0010 0.0002
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## 300 1.1232 nan 0.0010 0.0002
## 320 1.1131 nan 0.0010 0.0002
## 340 1.1033 nan 0.0010 0.0002
## 360 1.0936 nan 0.0010 0.0002
## 380 1.0841 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0661 nan 0.0010 0.0002
## 440 1.0575 nan 0.0010 0.0002
## 460 1.0491 nan 0.0010 0.0001
## 480 1.0410 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0003
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2309 nan 0.0010 0.0002
## 140 1.2174 nan 0.0010 0.0003
## 160 1.2044 nan 0.0010 0.0003
## 180 1.1916 nan 0.0010 0.0003
## 200 1.1793 nan 0.0010 0.0003
## 220 1.1679 nan 0.0010 0.0002
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## 320 1.1147 nan 0.0010 0.0002
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## 360 1.0951 nan 0.0010 0.0002
## 380 1.0853 nan 0.0010 0.0002
## 400 1.0764 nan 0.0010 0.0002
## 420 1.0677 nan 0.0010 0.0002
## 440 1.0590 nan 0.0010 0.0002
## 460 1.0505 nan 0.0010 0.0002
## 480 1.0422 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2886 nan 0.0010 0.0004
## 60 1.2736 nan 0.0010 0.0004
## 80 1.2590 nan 0.0010 0.0004
## 100 1.2454 nan 0.0010 0.0003
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2185 nan 0.0010 0.0003
## 160 1.2059 nan 0.0010 0.0003
## 180 1.1934 nan 0.0010 0.0003
## 200 1.1814 nan 0.0010 0.0003
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1583 nan 0.0010 0.0002
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## 320 1.1161 nan 0.0010 0.0002
## 340 1.1062 nan 0.0010 0.0002
## 360 1.0967 nan 0.0010 0.0002
## 380 1.0872 nan 0.0010 0.0002
## 400 1.0782 nan 0.0010 0.0002
## 420 1.0695 nan 0.0010 0.0001
## 440 1.0608 nan 0.0010 0.0002
## 460 1.0523 nan 0.0010 0.0002
## 480 1.0441 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0003
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2547 nan 0.0010 0.0004
## 100 1.2395 nan 0.0010 0.0004
## 120 1.2252 nan 0.0010 0.0003
## 140 1.2109 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1838 nan 0.0010 0.0002
## 200 1.1709 nan 0.0010 0.0003
## 220 1.1586 nan 0.0010 0.0003
## 240 1.1468 nan 0.0010 0.0003
## 260 1.1351 nan 0.0010 0.0002
## 280 1.1236 nan 0.0010 0.0003
## 300 1.1124 nan 0.0010 0.0002
## 320 1.1015 nan 0.0010 0.0003
## 340 1.0907 nan 0.0010 0.0002
## 360 1.0804 nan 0.0010 0.0002
## 380 1.0703 nan 0.0010 0.0002
## 400 1.0606 nan 0.0010 0.0002
## 420 1.0513 nan 0.0010 0.0002
## 440 1.0422 nan 0.0010 0.0002
## 460 1.0333 nan 0.0010 0.0002
## 480 1.0246 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2696 nan 0.0010 0.0004
## 80 1.2539 nan 0.0010 0.0004
## 100 1.2392 nan 0.0010 0.0003
## 120 1.2245 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1965 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1579 nan 0.0010 0.0002
## 240 1.1458 nan 0.0010 0.0002
## 260 1.1340 nan 0.0010 0.0003
## 280 1.1225 nan 0.0010 0.0003
## 300 1.1117 nan 0.0010 0.0002
## 320 1.1012 nan 0.0010 0.0002
## 340 1.0906 nan 0.0010 0.0003
## 360 1.0803 nan 0.0010 0.0002
## 380 1.0707 nan 0.0010 0.0002
## 400 1.0610 nan 0.0010 0.0002
## 420 1.0516 nan 0.0010 0.0002
## 440 1.0428 nan 0.0010 0.0002
## 460 1.0342 nan 0.0010 0.0002
## 480 1.0256 nan 0.0010 0.0002
## 500 1.0173 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0003
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2116 nan 0.0010 0.0002
## 160 1.1980 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1721 nan 0.0010 0.0003
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1478 nan 0.0010 0.0003
## 260 1.1359 nan 0.0010 0.0003
## 280 1.1250 nan 0.0010 0.0003
## 300 1.1140 nan 0.0010 0.0002
## 320 1.1038 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0832 nan 0.0010 0.0002
## 380 1.0733 nan 0.0010 0.0002
## 400 1.0637 nan 0.0010 0.0002
## 420 1.0544 nan 0.0010 0.0002
## 440 1.0454 nan 0.0010 0.0002
## 460 1.0365 nan 0.0010 0.0002
## 480 1.0278 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0004
## 100 1.2352 nan 0.0010 0.0004
## 120 1.2199 nan 0.0010 0.0003
## 140 1.2051 nan 0.0010 0.0004
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1768 nan 0.0010 0.0003
## 200 1.1631 nan 0.0010 0.0003
## 220 1.1499 nan 0.0010 0.0003
## 240 1.1373 nan 0.0010 0.0003
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## 280 1.1129 nan 0.0010 0.0003
## 300 1.1015 nan 0.0010 0.0002
## 320 1.0903 nan 0.0010 0.0002
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0689 nan 0.0010 0.0002
## 380 1.0586 nan 0.0010 0.0002
## 400 1.0484 nan 0.0010 0.0002
## 420 1.0384 nan 0.0010 0.0002
## 440 1.0290 nan 0.0010 0.0002
## 460 1.0196 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2522 nan 0.0010 0.0004
## 100 1.2363 nan 0.0010 0.0004
## 120 1.2208 nan 0.0010 0.0004
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1915 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0003
## 200 1.1641 nan 0.0010 0.0003
## 220 1.1511 nan 0.0010 0.0003
## 240 1.1387 nan 0.0010 0.0002
## 260 1.1265 nan 0.0010 0.0003
## 280 1.1145 nan 0.0010 0.0003
## 300 1.1031 nan 0.0010 0.0002
## 320 1.0919 nan 0.0010 0.0002
## 340 1.0809 nan 0.0010 0.0002
## 360 1.0703 nan 0.0010 0.0002
## 380 1.0600 nan 0.0010 0.0002
## 400 1.0501 nan 0.0010 0.0002
## 420 1.0403 nan 0.0010 0.0002
## 440 1.0307 nan 0.0010 0.0002
## 460 1.0216 nan 0.0010 0.0002
## 480 1.0126 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0003
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2073 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1790 nan 0.0010 0.0003
## 200 1.1657 nan 0.0010 0.0002
## 220 1.1528 nan 0.0010 0.0003
## 240 1.1405 nan 0.0010 0.0002
## 260 1.1283 nan 0.0010 0.0003
## 280 1.1165 nan 0.0010 0.0003
## 300 1.1051 nan 0.0010 0.0002
## 320 1.0942 nan 0.0010 0.0003
## 340 1.0836 nan 0.0010 0.0002
## 360 1.0730 nan 0.0010 0.0002
## 380 1.0629 nan 0.0010 0.0002
## 400 1.0529 nan 0.0010 0.0002
## 420 1.0432 nan 0.0010 0.0002
## 440 1.0338 nan 0.0010 0.0002
## 460 1.0246 nan 0.0010 0.0002
## 480 1.0159 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3044 nan 0.0100 0.0036
## 3 1.2966 nan 0.0100 0.0034
## 4 1.2881 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0039
## 6 1.2729 nan 0.0100 0.0033
## 7 1.2658 nan 0.0100 0.0031
## 8 1.2583 nan 0.0100 0.0034
## 9 1.2509 nan 0.0100 0.0033
## 10 1.2438 nan 0.0100 0.0033
## 20 1.1806 nan 0.0100 0.0026
## 40 1.0759 nan 0.0100 0.0019
## 60 0.9953 nan 0.0100 0.0013
## 80 0.9336 nan 0.0100 0.0008
## 100 0.8835 nan 0.0100 0.0009
## 120 0.8425 nan 0.0100 0.0007
## 140 0.8094 nan 0.0100 0.0006
## 160 0.7804 nan 0.0100 0.0005
## 180 0.7568 nan 0.0100 0.0001
## 200 0.7348 nan 0.0100 0.0002
## 220 0.7169 nan 0.0100 -0.0001
## 240 0.6996 nan 0.0100 -0.0000
## 260 0.6828 nan 0.0100 0.0000
## 280 0.6679 nan 0.0100 0.0001
## 300 0.6532 nan 0.0100 0.0002
## 320 0.6408 nan 0.0100 -0.0000
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## 360 0.6172 nan 0.0100 0.0001
## 380 0.6061 nan 0.0100 0.0000
## 400 0.5961 nan 0.0100 0.0000
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## 480 0.5606 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0037
## 2 1.3041 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2879 nan 0.0100 0.0038
## 5 1.2796 nan 0.0100 0.0038
## 6 1.2723 nan 0.0100 0.0034
## 7 1.2653 nan 0.0100 0.0030
## 8 1.2581 nan 0.0100 0.0032
## 9 1.2503 nan 0.0100 0.0034
## 10 1.2428 nan 0.0100 0.0032
## 20 1.1784 nan 0.0100 0.0024
## 40 1.0732 nan 0.0100 0.0018
## 60 0.9941 nan 0.0100 0.0014
## 80 0.9330 nan 0.0100 0.0008
## 100 0.8827 nan 0.0100 0.0008
## 120 0.8422 nan 0.0100 0.0006
## 140 0.8079 nan 0.0100 0.0005
## 160 0.7794 nan 0.0100 0.0003
## 180 0.7559 nan 0.0100 0.0002
## 200 0.7348 nan 0.0100 0.0000
## 220 0.7166 nan 0.0100 -0.0000
## 240 0.6997 nan 0.0100 0.0001
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## 280 0.6709 nan 0.0100 0.0000
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## 320 0.6462 nan 0.0100 -0.0000
## 340 0.6356 nan 0.0100 0.0000
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## 380 0.6155 nan 0.0100 0.0001
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## 460 0.5787 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0036
## 2 1.3038 nan 0.0100 0.0034
## 3 1.2964 nan 0.0100 0.0033
## 4 1.2888 nan 0.0100 0.0038
## 5 1.2811 nan 0.0100 0.0033
## 6 1.2732 nan 0.0100 0.0036
## 7 1.2663 nan 0.0100 0.0032
## 8 1.2590 nan 0.0100 0.0035
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## 10 1.2443 nan 0.0100 0.0032
## 20 1.1790 nan 0.0100 0.0024
## 40 1.0771 nan 0.0100 0.0020
## 60 0.9993 nan 0.0100 0.0012
## 80 0.9368 nan 0.0100 0.0009
## 100 0.8878 nan 0.0100 0.0007
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## 180 0.7612 nan 0.0100 0.0003
## 200 0.7401 nan 0.0100 0.0000
## 220 0.7220 nan 0.0100 -0.0001
## 240 0.7051 nan 0.0100 -0.0000
## 260 0.6901 nan 0.0100 0.0001
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## 300 0.6645 nan 0.0100 0.0001
## 320 0.6540 nan 0.0100 -0.0001
## 340 0.6430 nan 0.0100 -0.0002
## 360 0.6320 nan 0.0100 -0.0001
## 380 0.6227 nan 0.0100 0.0000
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## 460 0.5881 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3026 nan 0.0100 0.0041
## 3 1.2937 nan 0.0100 0.0040
## 4 1.2852 nan 0.0100 0.0041
## 5 1.2770 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0034
## 7 1.2612 nan 0.0100 0.0037
## 8 1.2533 nan 0.0100 0.0034
## 9 1.2450 nan 0.0100 0.0036
## 10 1.2372 nan 0.0100 0.0032
## 20 1.1681 nan 0.0100 0.0029
## 40 1.0579 nan 0.0100 0.0021
## 60 0.9747 nan 0.0100 0.0014
## 80 0.9091 nan 0.0100 0.0010
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## 140 0.7789 nan 0.0100 0.0003
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## 240 0.6594 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0033
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2957 nan 0.0100 0.0036
## 4 1.2875 nan 0.0100 0.0036
## 5 1.2788 nan 0.0100 0.0034
## 6 1.2710 nan 0.0100 0.0038
## 7 1.2631 nan 0.0100 0.0035
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## 20 1.1710 nan 0.0100 0.0025
## 40 1.0597 nan 0.0100 0.0019
## 60 0.9785 nan 0.0100 0.0014
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## 140 0.7841 nan 0.0100 0.0003
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## 280 0.6352 nan 0.0100 0.0001
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## 320 0.6084 nan 0.0100 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0041
## 3 1.2952 nan 0.0100 0.0040
## 4 1.2869 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0033
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## 7 1.2628 nan 0.0100 0.0035
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## 40 1.0644 nan 0.0100 0.0021
## 60 0.9824 nan 0.0100 0.0015
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## 400 0.5695 nan 0.0100 -0.0001
## 420 0.5594 nan 0.0100 0.0000
## 440 0.5499 nan 0.0100 -0.0001
## 460 0.5410 nan 0.0100 -0.0002
## 480 0.5321 nan 0.0100 -0.0001
## 500 0.5224 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0044
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2844 nan 0.0100 0.0042
## 5 1.2761 nan 0.0100 0.0039
## 6 1.2680 nan 0.0100 0.0035
## 7 1.2599 nan 0.0100 0.0038
## 8 1.2519 nan 0.0100 0.0035
## 9 1.2439 nan 0.0100 0.0039
## 10 1.2358 nan 0.0100 0.0036
## 20 1.1632 nan 0.0100 0.0030
## 40 1.0480 nan 0.0100 0.0021
## 60 0.9606 nan 0.0100 0.0016
## 80 0.8928 nan 0.0100 0.0013
## 100 0.8379 nan 0.0100 0.0006
## 120 0.7924 nan 0.0100 0.0004
## 140 0.7549 nan 0.0100 0.0003
## 160 0.7229 nan 0.0100 0.0002
## 180 0.6939 nan 0.0100 0.0004
## 200 0.6695 nan 0.0100 0.0001
## 220 0.6468 nan 0.0100 -0.0000
## 240 0.6271 nan 0.0100 0.0000
## 260 0.6099 nan 0.0100 0.0001
## 280 0.5935 nan 0.0100 0.0001
## 300 0.5777 nan 0.0100 0.0000
## 320 0.5639 nan 0.0100 0.0000
## 340 0.5503 nan 0.0100 0.0002
## 360 0.5370 nan 0.0100 0.0001
## 380 0.5239 nan 0.0100 0.0000
## 400 0.5120 nan 0.0100 -0.0000
## 420 0.5003 nan 0.0100 -0.0002
## 440 0.4903 nan 0.0100 -0.0001
## 460 0.4794 nan 0.0100 0.0000
## 480 0.4699 nan 0.0100 -0.0001
## 500 0.4602 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3030 nan 0.0100 0.0034
## 3 1.2950 nan 0.0100 0.0038
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2775 nan 0.0100 0.0035
## 6 1.2688 nan 0.0100 0.0038
## 7 1.2610 nan 0.0100 0.0037
## 8 1.2532 nan 0.0100 0.0033
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2374 nan 0.0100 0.0037
## 20 1.1649 nan 0.0100 0.0030
## 40 1.0516 nan 0.0100 0.0020
## 60 0.9633 nan 0.0100 0.0015
## 80 0.8936 nan 0.0100 0.0013
## 100 0.8396 nan 0.0100 0.0007
## 120 0.7955 nan 0.0100 0.0008
## 140 0.7576 nan 0.0100 0.0004
## 160 0.7269 nan 0.0100 0.0003
## 180 0.6992 nan 0.0100 0.0001
## 200 0.6766 nan 0.0100 0.0001
## 220 0.6546 nan 0.0100 0.0000
## 240 0.6378 nan 0.0100 -0.0001
## 260 0.6203 nan 0.0100 0.0001
## 280 0.6049 nan 0.0100 0.0002
## 300 0.5900 nan 0.0100 0.0000
## 320 0.5762 nan 0.0100 0.0001
## 340 0.5622 nan 0.0100 -0.0000
## 360 0.5495 nan 0.0100 -0.0000
## 380 0.5372 nan 0.0100 0.0000
## 400 0.5250 nan 0.0100 -0.0000
## 420 0.5130 nan 0.0100 -0.0000
## 440 0.5020 nan 0.0100 0.0000
## 460 0.4911 nan 0.0100 0.0000
## 480 0.4808 nan 0.0100 -0.0000
## 500 0.4709 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3033 nan 0.0100 0.0039
## 3 1.2943 nan 0.0100 0.0040
## 4 1.2853 nan 0.0100 0.0039
## 5 1.2764 nan 0.0100 0.0040
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2599 nan 0.0100 0.0032
## 8 1.2519 nan 0.0100 0.0037
## 9 1.2440 nan 0.0100 0.0033
## 10 1.2362 nan 0.0100 0.0036
## 20 1.1650 nan 0.0100 0.0029
## 40 1.0533 nan 0.0100 0.0019
## 60 0.9671 nan 0.0100 0.0016
## 80 0.9022 nan 0.0100 0.0010
## 100 0.8470 nan 0.0100 0.0010
## 120 0.8027 nan 0.0100 0.0007
## 140 0.7661 nan 0.0100 0.0005
## 160 0.7356 nan 0.0100 0.0004
## 180 0.7083 nan 0.0100 0.0003
## 200 0.6851 nan 0.0100 0.0003
## 220 0.6647 nan 0.0100 0.0000
## 240 0.6458 nan 0.0100 0.0000
## 260 0.6284 nan 0.0100 0.0001
## 280 0.6124 nan 0.0100 0.0001
## 300 0.5969 nan 0.0100 0.0000
## 320 0.5812 nan 0.0100 0.0000
## 340 0.5674 nan 0.0100 -0.0000
## 360 0.5548 nan 0.0100 -0.0001
## 380 0.5433 nan 0.0100 -0.0000
## 400 0.5323 nan 0.0100 -0.0001
## 420 0.5214 nan 0.0100 -0.0001
## 440 0.5104 nan 0.0100 -0.0002
## 460 0.5007 nan 0.0100 0.0000
## 480 0.4913 nan 0.0100 -0.0001
## 500 0.4809 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0394
## 2 1.1743 nan 0.1000 0.0312
## 3 1.1206 nan 0.1000 0.0246
## 4 1.0750 nan 0.1000 0.0193
## 5 1.0319 nan 0.1000 0.0182
## 6 0.9978 nan 0.1000 0.0139
## 7 0.9649 nan 0.1000 0.0126
## 8 0.9335 nan 0.1000 0.0115
## 9 0.9061 nan 0.1000 0.0103
## 10 0.8819 nan 0.1000 0.0089
## 20 0.7356 nan 0.1000 0.0033
## 40 0.6047 nan 0.1000 -0.0006
## 60 0.5215 nan 0.1000 -0.0015
## 80 0.4611 nan 0.1000 -0.0011
## 100 0.4097 nan 0.1000 0.0003
## 120 0.3647 nan 0.1000 -0.0004
## 140 0.3256 nan 0.1000 -0.0010
## 160 0.2962 nan 0.1000 -0.0015
## 180 0.2685 nan 0.1000 -0.0002
## 200 0.2436 nan 0.1000 -0.0002
## 220 0.2213 nan 0.1000 -0.0005
## 240 0.2030 nan 0.1000 -0.0006
## 260 0.1868 nan 0.1000 -0.0004
## 280 0.1725 nan 0.1000 -0.0002
## 300 0.1588 nan 0.1000 -0.0003
## 320 0.1458 nan 0.1000 -0.0002
## 340 0.1346 nan 0.1000 -0.0002
## 360 0.1227 nan 0.1000 -0.0001
## 380 0.1134 nan 0.1000 -0.0003
## 400 0.1043 nan 0.1000 0.0002
## 420 0.0963 nan 0.1000 -0.0002
## 440 0.0888 nan 0.1000 -0.0004
## 460 0.0820 nan 0.1000 -0.0000
## 480 0.0758 nan 0.1000 -0.0002
## 500 0.0701 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2509 nan 0.1000 0.0334
## 2 1.1864 nan 0.1000 0.0265
## 3 1.1247 nan 0.1000 0.0266
## 4 1.0757 nan 0.1000 0.0211
## 5 1.0304 nan 0.1000 0.0198
## 6 0.9920 nan 0.1000 0.0164
## 7 0.9586 nan 0.1000 0.0097
## 8 0.9312 nan 0.1000 0.0104
## 9 0.9080 nan 0.1000 0.0079
## 10 0.8838 nan 0.1000 0.0092
## 20 0.7409 nan 0.1000 0.0001
## 40 0.6120 nan 0.1000 -0.0013
## 60 0.5359 nan 0.1000 -0.0017
## 80 0.4693 nan 0.1000 -0.0009
## 100 0.4200 nan 0.1000 -0.0011
## 120 0.3749 nan 0.1000 -0.0011
## 140 0.3340 nan 0.1000 -0.0002
## 160 0.3011 nan 0.1000 -0.0004
## 180 0.2701 nan 0.1000 -0.0006
## 200 0.2468 nan 0.1000 -0.0002
## 220 0.2284 nan 0.1000 -0.0010
## 240 0.2086 nan 0.1000 -0.0007
## 260 0.1923 nan 0.1000 -0.0005
## 280 0.1757 nan 0.1000 -0.0003
## 300 0.1617 nan 0.1000 -0.0007
## 320 0.1493 nan 0.1000 -0.0006
## 340 0.1386 nan 0.1000 -0.0006
## 360 0.1261 nan 0.1000 -0.0005
## 380 0.1172 nan 0.1000 -0.0003
## 400 0.1083 nan 0.1000 -0.0002
## 420 0.1008 nan 0.1000 -0.0002
## 440 0.0931 nan 0.1000 -0.0003
## 460 0.0852 nan 0.1000 -0.0000
## 480 0.0788 nan 0.1000 -0.0003
## 500 0.0730 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0371
## 2 1.1699 nan 0.1000 0.0306
## 3 1.1148 nan 0.1000 0.0245
## 4 1.0653 nan 0.1000 0.0239
## 5 1.0270 nan 0.1000 0.0159
## 6 0.9959 nan 0.1000 0.0146
## 7 0.9591 nan 0.1000 0.0152
## 8 0.9299 nan 0.1000 0.0109
## 9 0.9045 nan 0.1000 0.0115
## 10 0.8811 nan 0.1000 0.0076
## 20 0.7402 nan 0.1000 0.0020
## 40 0.6166 nan 0.1000 0.0006
## 60 0.5365 nan 0.1000 -0.0014
## 80 0.4775 nan 0.1000 -0.0010
## 100 0.4262 nan 0.1000 -0.0007
## 120 0.3816 nan 0.1000 -0.0012
## 140 0.3423 nan 0.1000 -0.0006
## 160 0.3105 nan 0.1000 -0.0002
## 180 0.2838 nan 0.1000 -0.0025
## 200 0.2586 nan 0.1000 -0.0018
## 220 0.2361 nan 0.1000 -0.0010
## 240 0.2146 nan 0.1000 -0.0004
## 260 0.1963 nan 0.1000 -0.0002
## 280 0.1811 nan 0.1000 -0.0005
## 300 0.1683 nan 0.1000 -0.0002
## 320 0.1544 nan 0.1000 -0.0002
## 340 0.1416 nan 0.1000 -0.0008
## 360 0.1319 nan 0.1000 -0.0004
## 380 0.1218 nan 0.1000 -0.0003
## 400 0.1137 nan 0.1000 -0.0004
## 420 0.1058 nan 0.1000 -0.0003
## 440 0.0988 nan 0.1000 -0.0002
## 460 0.0911 nan 0.1000 -0.0003
## 480 0.0853 nan 0.1000 -0.0005
## 500 0.0796 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2240 nan 0.1000 0.0416
## 2 1.1543 nan 0.1000 0.0325
## 3 1.0932 nan 0.1000 0.0246
## 4 1.0470 nan 0.1000 0.0190
## 5 1.0070 nan 0.1000 0.0153
## 6 0.9696 nan 0.1000 0.0177
## 7 0.9321 nan 0.1000 0.0131
## 8 0.9023 nan 0.1000 0.0113
## 9 0.8739 nan 0.1000 0.0121
## 10 0.8479 nan 0.1000 0.0099
## 20 0.6962 nan 0.1000 0.0026
## 40 0.5499 nan 0.1000 0.0006
## 60 0.4632 nan 0.1000 -0.0001
## 80 0.3970 nan 0.1000 -0.0007
## 100 0.3441 nan 0.1000 -0.0005
## 120 0.3019 nan 0.1000 -0.0006
## 140 0.2683 nan 0.1000 -0.0006
## 160 0.2363 nan 0.1000 -0.0014
## 180 0.2111 nan 0.1000 -0.0014
## 200 0.1883 nan 0.1000 0.0001
## 220 0.1645 nan 0.1000 -0.0004
## 240 0.1472 nan 0.1000 -0.0003
## 260 0.1319 nan 0.1000 -0.0002
## 280 0.1177 nan 0.1000 -0.0001
## 300 0.1064 nan 0.1000 -0.0005
## 320 0.0968 nan 0.1000 -0.0002
## 340 0.0870 nan 0.1000 -0.0003
## 360 0.0782 nan 0.1000 -0.0000
## 380 0.0711 nan 0.1000 -0.0002
## 400 0.0641 nan 0.1000 -0.0001
## 420 0.0588 nan 0.1000 0.0000
## 440 0.0529 nan 0.1000 -0.0000
## 460 0.0485 nan 0.1000 -0.0001
## 480 0.0436 nan 0.1000 0.0000
## 500 0.0395 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0387
## 2 1.1704 nan 0.1000 0.0278
## 3 1.1123 nan 0.1000 0.0270
## 4 1.0574 nan 0.1000 0.0219
## 5 1.0140 nan 0.1000 0.0174
## 6 0.9747 nan 0.1000 0.0181
## 7 0.9440 nan 0.1000 0.0106
## 8 0.9158 nan 0.1000 0.0102
## 9 0.8895 nan 0.1000 0.0076
## 10 0.8649 nan 0.1000 0.0092
## 20 0.7068 nan 0.1000 0.0024
## 40 0.5659 nan 0.1000 -0.0015
## 60 0.4757 nan 0.1000 -0.0008
## 80 0.4002 nan 0.1000 -0.0015
## 100 0.3511 nan 0.1000 -0.0009
## 120 0.3024 nan 0.1000 -0.0004
## 140 0.2657 nan 0.1000 -0.0010
## 160 0.2384 nan 0.1000 -0.0005
## 180 0.2101 nan 0.1000 -0.0011
## 200 0.1884 nan 0.1000 -0.0007
## 220 0.1676 nan 0.1000 -0.0006
## 240 0.1497 nan 0.1000 -0.0010
## 260 0.1351 nan 0.1000 -0.0006
## 280 0.1207 nan 0.1000 -0.0003
## 300 0.1095 nan 0.1000 -0.0007
## 320 0.0987 nan 0.1000 -0.0003
## 340 0.0892 nan 0.1000 -0.0005
## 360 0.0813 nan 0.1000 -0.0002
## 380 0.0734 nan 0.1000 -0.0004
## 400 0.0671 nan 0.1000 -0.0002
## 420 0.0614 nan 0.1000 -0.0002
## 440 0.0557 nan 0.1000 -0.0003
## 460 0.0504 nan 0.1000 -0.0002
## 480 0.0459 nan 0.1000 -0.0001
## 500 0.0412 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0362
## 2 1.1707 nan 0.1000 0.0342
## 3 1.1115 nan 0.1000 0.0264
## 4 1.0620 nan 0.1000 0.0233
## 5 1.0164 nan 0.1000 0.0217
## 6 0.9767 nan 0.1000 0.0165
## 7 0.9401 nan 0.1000 0.0141
## 8 0.9081 nan 0.1000 0.0113
## 9 0.8805 nan 0.1000 0.0121
## 10 0.8580 nan 0.1000 0.0084
## 20 0.7049 nan 0.1000 0.0035
## 40 0.5691 nan 0.1000 0.0000
## 60 0.4878 nan 0.1000 -0.0007
## 80 0.4201 nan 0.1000 0.0001
## 100 0.3689 nan 0.1000 -0.0017
## 120 0.3226 nan 0.1000 -0.0012
## 140 0.2825 nan 0.1000 -0.0004
## 160 0.2475 nan 0.1000 -0.0005
## 180 0.2191 nan 0.1000 -0.0003
## 200 0.1944 nan 0.1000 -0.0007
## 220 0.1735 nan 0.1000 -0.0002
## 240 0.1563 nan 0.1000 -0.0009
## 260 0.1401 nan 0.1000 -0.0006
## 280 0.1277 nan 0.1000 -0.0001
## 300 0.1149 nan 0.1000 -0.0004
## 320 0.1047 nan 0.1000 -0.0003
## 340 0.0944 nan 0.1000 -0.0004
## 360 0.0852 nan 0.1000 -0.0002
## 380 0.0772 nan 0.1000 -0.0003
## 400 0.0697 nan 0.1000 -0.0003
## 420 0.0637 nan 0.1000 -0.0003
## 440 0.0584 nan 0.1000 -0.0003
## 460 0.0535 nan 0.1000 -0.0002
## 480 0.0486 nan 0.1000 -0.0001
## 500 0.0443 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2295 nan 0.1000 0.0426
## 2 1.1476 nan 0.1000 0.0343
## 3 1.0883 nan 0.1000 0.0260
## 4 1.0358 nan 0.1000 0.0209
## 5 0.9949 nan 0.1000 0.0174
## 6 0.9568 nan 0.1000 0.0160
## 7 0.9169 nan 0.1000 0.0151
## 8 0.8862 nan 0.1000 0.0135
## 9 0.8568 nan 0.1000 0.0125
## 10 0.8307 nan 0.1000 0.0074
## 20 0.6742 nan 0.1000 0.0003
## 40 0.5148 nan 0.1000 -0.0013
## 60 0.4146 nan 0.1000 0.0005
## 80 0.3489 nan 0.1000 -0.0006
## 100 0.2939 nan 0.1000 -0.0013
## 120 0.2480 nan 0.1000 -0.0012
## 140 0.2121 nan 0.1000 -0.0001
## 160 0.1834 nan 0.1000 -0.0001
## 180 0.1580 nan 0.1000 -0.0004
## 200 0.1371 nan 0.1000 -0.0001
## 220 0.1170 nan 0.1000 -0.0002
## 240 0.1032 nan 0.1000 -0.0001
## 260 0.0917 nan 0.1000 -0.0003
## 280 0.0816 nan 0.1000 0.0000
## 300 0.0726 nan 0.1000 -0.0002
## 320 0.0637 nan 0.1000 -0.0001
## 340 0.0560 nan 0.1000 -0.0002
## 360 0.0498 nan 0.1000 -0.0001
## 380 0.0441 nan 0.1000 -0.0000
## 400 0.0394 nan 0.1000 -0.0000
## 420 0.0353 nan 0.1000 -0.0000
## 440 0.0313 nan 0.1000 -0.0001
## 460 0.0278 nan 0.1000 -0.0001
## 480 0.0251 nan 0.1000 0.0000
## 500 0.0224 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2303 nan 0.1000 0.0356
## 2 1.1605 nan 0.1000 0.0299
## 3 1.0959 nan 0.1000 0.0302
## 4 1.0394 nan 0.1000 0.0224
## 5 0.9968 nan 0.1000 0.0187
## 6 0.9569 nan 0.1000 0.0177
## 7 0.9254 nan 0.1000 0.0114
## 8 0.8951 nan 0.1000 0.0112
## 9 0.8689 nan 0.1000 0.0094
## 10 0.8404 nan 0.1000 0.0106
## 20 0.6839 nan 0.1000 0.0009
## 40 0.5214 nan 0.1000 0.0004
## 60 0.4227 nan 0.1000 -0.0018
## 80 0.3522 nan 0.1000 -0.0008
## 100 0.3014 nan 0.1000 0.0001
## 120 0.2602 nan 0.1000 -0.0004
## 140 0.2163 nan 0.1000 -0.0006
## 160 0.1862 nan 0.1000 -0.0009
## 180 0.1606 nan 0.1000 0.0000
## 200 0.1407 nan 0.1000 -0.0008
## 220 0.1222 nan 0.1000 -0.0002
## 240 0.1068 nan 0.1000 -0.0001
## 260 0.0941 nan 0.1000 -0.0005
## 280 0.0829 nan 0.1000 -0.0001
## 300 0.0730 nan 0.1000 -0.0002
## 320 0.0631 nan 0.1000 -0.0002
## 340 0.0563 nan 0.1000 -0.0002
## 360 0.0500 nan 0.1000 -0.0002
## 380 0.0442 nan 0.1000 -0.0002
## 400 0.0398 nan 0.1000 -0.0001
## 420 0.0353 nan 0.1000 -0.0002
## 440 0.0312 nan 0.1000 -0.0001
## 460 0.0279 nan 0.1000 -0.0001
## 480 0.0252 nan 0.1000 -0.0001
## 500 0.0226 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2321 nan 0.1000 0.0411
## 2 1.1604 nan 0.1000 0.0315
## 3 1.1016 nan 0.1000 0.0271
## 4 1.0512 nan 0.1000 0.0221
## 5 1.0049 nan 0.1000 0.0191
## 6 0.9673 nan 0.1000 0.0161
## 7 0.9309 nan 0.1000 0.0152
## 8 0.9066 nan 0.1000 0.0077
## 9 0.8819 nan 0.1000 0.0096
## 10 0.8556 nan 0.1000 0.0104
## 20 0.6852 nan 0.1000 0.0013
## 40 0.5429 nan 0.1000 0.0005
## 60 0.4519 nan 0.1000 -0.0017
## 80 0.3700 nan 0.1000 -0.0013
## 100 0.3108 nan 0.1000 -0.0003
## 120 0.2650 nan 0.1000 -0.0012
## 140 0.2295 nan 0.1000 -0.0010
## 160 0.2020 nan 0.1000 -0.0009
## 180 0.1750 nan 0.1000 -0.0008
## 200 0.1515 nan 0.1000 -0.0000
## 220 0.1328 nan 0.1000 -0.0003
## 240 0.1171 nan 0.1000 -0.0005
## 260 0.1030 nan 0.1000 -0.0004
## 280 0.0904 nan 0.1000 -0.0005
## 300 0.0808 nan 0.1000 -0.0001
## 320 0.0725 nan 0.1000 -0.0003
## 340 0.0647 nan 0.1000 -0.0002
## 360 0.0580 nan 0.1000 -0.0003
## 380 0.0519 nan 0.1000 -0.0004
## 400 0.0456 nan 0.1000 -0.0001
## 420 0.0408 nan 0.1000 -0.0001
## 440 0.0364 nan 0.1000 -0.0003
## 460 0.0330 nan 0.1000 -0.0001
## 480 0.0299 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2237 nan 0.0010 0.0003
## 140 1.2093 nan 0.0010 0.0003
## 160 1.1954 nan 0.0010 0.0003
## 180 1.1820 nan 0.0010 0.0003
## 200 1.1691 nan 0.0010 0.0003
## 220 1.1561 nan 0.0010 0.0003
## 240 1.1437 nan 0.0010 0.0003
## 260 1.1317 nan 0.0010 0.0003
## 280 1.1200 nan 0.0010 0.0002
## 300 1.1086 nan 0.0010 0.0003
## 320 1.0977 nan 0.0010 0.0002
## 340 1.0870 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0665 nan 0.0010 0.0002
## 400 1.0565 nan 0.0010 0.0002
## 420 1.0469 nan 0.0010 0.0002
## 440 1.0374 nan 0.0010 0.0002
## 460 1.0283 nan 0.0010 0.0002
## 480 1.0195 nan 0.0010 0.0002
## 500 1.0107 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0003
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2389 nan 0.0010 0.0003
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2092 nan 0.0010 0.0003
## 160 1.1952 nan 0.0010 0.0003
## 180 1.1817 nan 0.0010 0.0003
## 200 1.1685 nan 0.0010 0.0003
## 220 1.1560 nan 0.0010 0.0003
## 240 1.1437 nan 0.0010 0.0003
## 260 1.1319 nan 0.0010 0.0002
## 280 1.1200 nan 0.0010 0.0003
## 300 1.1087 nan 0.0010 0.0003
## 320 1.0976 nan 0.0010 0.0002
## 340 1.0868 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0664 nan 0.0010 0.0002
## 400 1.0565 nan 0.0010 0.0002
## 420 1.0469 nan 0.0010 0.0002
## 440 1.0377 nan 0.0010 0.0002
## 460 1.0288 nan 0.0010 0.0002
## 480 1.0198 nan 0.0010 0.0002
## 500 1.0110 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0004
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2117 nan 0.0010 0.0004
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1845 nan 0.0010 0.0003
## 200 1.1714 nan 0.0010 0.0003
## 220 1.1585 nan 0.0010 0.0003
## 240 1.1461 nan 0.0010 0.0003
## 260 1.1342 nan 0.0010 0.0002
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1115 nan 0.0010 0.0002
## 320 1.1006 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
## 360 1.0798 nan 0.0010 0.0002
## 380 1.0697 nan 0.0010 0.0002
## 400 1.0597 nan 0.0010 0.0002
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## 480 1.0230 nan 0.0010 0.0002
## 500 1.0143 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0004
## 100 1.2348 nan 0.0010 0.0004
## 120 1.2188 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0004
## 160 1.1886 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
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## 280 1.1096 nan 0.0010 0.0003
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## 320 1.0861 nan 0.0010 0.0003
## 340 1.0746 nan 0.0010 0.0002
## 360 1.0635 nan 0.0010 0.0003
## 380 1.0528 nan 0.0010 0.0002
## 400 1.0423 nan 0.0010 0.0002
## 420 1.0322 nan 0.0010 0.0002
## 440 1.0224 nan 0.0010 0.0002
## 460 1.0126 nan 0.0010 0.0002
## 480 1.0033 nan 0.0010 0.0002
## 500 0.9941 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2038 nan 0.0010 0.0003
## 160 1.1890 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1221 nan 0.0010 0.0002
## 280 1.1099 nan 0.0010 0.0003
## 300 1.0979 nan 0.0010 0.0002
## 320 1.0862 nan 0.0010 0.0002
## 340 1.0746 nan 0.0010 0.0002
## 360 1.0635 nan 0.0010 0.0002
## 380 1.0530 nan 0.0010 0.0002
## 400 1.0426 nan 0.0010 0.0002
## 420 1.0325 nan 0.0010 0.0002
## 440 1.0228 nan 0.0010 0.0002
## 460 1.0133 nan 0.0010 0.0002
## 480 1.0039 nan 0.0010 0.0002
## 500 0.9948 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0003
## 80 1.2515 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0004
## 120 1.2201 nan 0.0010 0.0004
## 140 1.2049 nan 0.0010 0.0003
## 160 1.1904 nan 0.0010 0.0003
## 180 1.1764 nan 0.0010 0.0003
## 200 1.1628 nan 0.0010 0.0003
## 220 1.1493 nan 0.0010 0.0003
## 240 1.1364 nan 0.0010 0.0003
## 260 1.1241 nan 0.0010 0.0003
## 280 1.1119 nan 0.0010 0.0003
## 300 1.1002 nan 0.0010 0.0003
## 320 1.0889 nan 0.0010 0.0003
## 340 1.0779 nan 0.0010 0.0002
## 360 1.0669 nan 0.0010 0.0002
## 380 1.0565 nan 0.0010 0.0003
## 400 1.0460 nan 0.0010 0.0002
## 420 1.0360 nan 0.0010 0.0002
## 440 1.0260 nan 0.0010 0.0002
## 460 1.0162 nan 0.0010 0.0002
## 480 1.0072 nan 0.0010 0.0002
## 500 0.9980 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0005
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2646 nan 0.0010 0.0005
## 80 1.2467 nan 0.0010 0.0004
## 100 1.2300 nan 0.0010 0.0003
## 120 1.2134 nan 0.0010 0.0003
## 140 1.1974 nan 0.0010 0.0003
## 160 1.1820 nan 0.0010 0.0003
## 180 1.1672 nan 0.0010 0.0004
## 200 1.1528 nan 0.0010 0.0003
## 220 1.1389 nan 0.0010 0.0003
## 240 1.1251 nan 0.0010 0.0003
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## 280 1.0992 nan 0.0010 0.0003
## 300 1.0869 nan 0.0010 0.0003
## 320 1.0746 nan 0.0010 0.0002
## 340 1.0630 nan 0.0010 0.0002
## 360 1.0517 nan 0.0010 0.0003
## 380 1.0407 nan 0.0010 0.0002
## 400 1.0296 nan 0.0010 0.0002
## 420 1.0193 nan 0.0010 0.0002
## 440 1.0090 nan 0.0010 0.0002
## 460 0.9989 nan 0.0010 0.0002
## 480 0.9890 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0005
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2652 nan 0.0010 0.0004
## 80 1.2477 nan 0.0010 0.0004
## 100 1.2310 nan 0.0010 0.0004
## 120 1.2147 nan 0.0010 0.0004
## 140 1.1990 nan 0.0010 0.0004
## 160 1.1836 nan 0.0010 0.0004
## 180 1.1687 nan 0.0010 0.0003
## 200 1.1542 nan 0.0010 0.0003
## 220 1.1405 nan 0.0010 0.0003
## 240 1.1272 nan 0.0010 0.0003
## 260 1.1140 nan 0.0010 0.0003
## 280 1.1012 nan 0.0010 0.0003
## 300 1.0887 nan 0.0010 0.0003
## 320 1.0767 nan 0.0010 0.0003
## 340 1.0651 nan 0.0010 0.0002
## 360 1.0539 nan 0.0010 0.0002
## 380 1.0429 nan 0.0010 0.0002
## 400 1.0320 nan 0.0010 0.0002
## 420 1.0216 nan 0.0010 0.0002
## 440 1.0112 nan 0.0010 0.0002
## 460 1.0011 nan 0.0010 0.0002
## 480 0.9915 nan 0.0010 0.0002
## 500 0.9820 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2659 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0004
## 100 1.2318 nan 0.0010 0.0004
## 120 1.2156 nan 0.0010 0.0004
## 140 1.2001 nan 0.0010 0.0003
## 160 1.1852 nan 0.0010 0.0003
## 180 1.1709 nan 0.0010 0.0003
## 200 1.1567 nan 0.0010 0.0003
## 220 1.1430 nan 0.0010 0.0003
## 240 1.1299 nan 0.0010 0.0003
## 260 1.1172 nan 0.0010 0.0003
## 280 1.1047 nan 0.0010 0.0002
## 300 1.0925 nan 0.0010 0.0003
## 320 1.0806 nan 0.0010 0.0003
## 340 1.0691 nan 0.0010 0.0003
## 360 1.0580 nan 0.0010 0.0003
## 380 1.0471 nan 0.0010 0.0002
## 400 1.0364 nan 0.0010 0.0002
## 420 1.0261 nan 0.0010 0.0002
## 440 1.0161 nan 0.0010 0.0002
## 460 1.0064 nan 0.0010 0.0002
## 480 0.9968 nan 0.0010 0.0002
## 500 0.9877 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0044
## 2 1.3035 nan 0.0100 0.0036
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2864 nan 0.0100 0.0040
## 5 1.2779 nan 0.0100 0.0040
## 6 1.2694 nan 0.0100 0.0039
## 7 1.2612 nan 0.0100 0.0036
## 8 1.2535 nan 0.0100 0.0034
## 9 1.2462 nan 0.0100 0.0033
## 10 1.2387 nan 0.0100 0.0034
## 20 1.1676 nan 0.0100 0.0026
## 40 1.0537 nan 0.0100 0.0023
## 60 0.9691 nan 0.0100 0.0014
## 80 0.9022 nan 0.0100 0.0013
## 100 0.8471 nan 0.0100 0.0010
## 120 0.8030 nan 0.0100 0.0008
## 140 0.7665 nan 0.0100 0.0005
## 160 0.7357 nan 0.0100 0.0004
## 180 0.7087 nan 0.0100 0.0004
## 200 0.6849 nan 0.0100 0.0002
## 220 0.6633 nan 0.0100 0.0001
## 240 0.6445 nan 0.0100 0.0001
## 260 0.6290 nan 0.0100 0.0003
## 280 0.6139 nan 0.0100 0.0001
## 300 0.5991 nan 0.0100 0.0002
## 320 0.5866 nan 0.0100 0.0000
## 340 0.5747 nan 0.0100 0.0001
## 360 0.5637 nan 0.0100 0.0001
## 380 0.5525 nan 0.0100 -0.0000
## 400 0.5420 nan 0.0100 0.0001
## 420 0.5324 nan 0.0100 0.0001
## 440 0.5232 nan 0.0100 -0.0000
## 460 0.5146 nan 0.0100 0.0000
## 480 0.5059 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0041
## 2 1.3026 nan 0.0100 0.0044
## 3 1.2943 nan 0.0100 0.0039
## 4 1.2858 nan 0.0100 0.0039
## 5 1.2771 nan 0.0100 0.0038
## 6 1.2685 nan 0.0100 0.0038
## 7 1.2607 nan 0.0100 0.0037
## 8 1.2526 nan 0.0100 0.0033
## 9 1.2451 nan 0.0100 0.0031
## 10 1.2374 nan 0.0100 0.0036
## 20 1.1691 nan 0.0100 0.0029
## 40 1.0574 nan 0.0100 0.0020
## 60 0.9704 nan 0.0100 0.0015
## 80 0.9026 nan 0.0100 0.0012
## 100 0.8494 nan 0.0100 0.0009
## 120 0.8056 nan 0.0100 0.0007
## 140 0.7699 nan 0.0100 0.0005
## 160 0.7403 nan 0.0100 0.0004
## 180 0.7136 nan 0.0100 0.0004
## 200 0.6903 nan 0.0100 0.0003
## 220 0.6701 nan 0.0100 0.0003
## 240 0.6519 nan 0.0100 0.0002
## 260 0.6360 nan 0.0100 0.0001
## 280 0.6219 nan 0.0100 0.0001
## 300 0.6095 nan 0.0100 0.0002
## 320 0.5975 nan 0.0100 0.0001
## 340 0.5855 nan 0.0100 -0.0001
## 360 0.5750 nan 0.0100 0.0001
## 380 0.5638 nan 0.0100 -0.0000
## 400 0.5548 nan 0.0100 0.0000
## 420 0.5444 nan 0.0100 -0.0000
## 440 0.5361 nan 0.0100 -0.0001
## 460 0.5280 nan 0.0100 -0.0001
## 480 0.5185 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0040
## 2 1.3039 nan 0.0100 0.0042
## 3 1.2952 nan 0.0100 0.0040
## 4 1.2862 nan 0.0100 0.0042
## 5 1.2775 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0037
## 7 1.2618 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0036
## 9 1.2465 nan 0.0100 0.0035
## 10 1.2392 nan 0.0100 0.0033
## 20 1.1698 nan 0.0100 0.0029
## 40 1.0588 nan 0.0100 0.0018
## 60 0.9730 nan 0.0100 0.0016
## 80 0.9044 nan 0.0100 0.0012
## 100 0.8525 nan 0.0100 0.0007
## 120 0.8087 nan 0.0100 0.0008
## 140 0.7723 nan 0.0100 0.0006
## 160 0.7420 nan 0.0100 0.0004
## 180 0.7160 nan 0.0100 0.0002
## 200 0.6940 nan 0.0100 0.0002
## 220 0.6739 nan 0.0100 0.0003
## 240 0.6560 nan 0.0100 0.0002
## 260 0.6403 nan 0.0100 0.0000
## 280 0.6265 nan 0.0100 0.0002
## 300 0.6133 nan 0.0100 0.0000
## 320 0.6014 nan 0.0100 -0.0002
## 340 0.5901 nan 0.0100 0.0001
## 360 0.5790 nan 0.0100 -0.0000
## 380 0.5693 nan 0.0100 0.0000
## 400 0.5596 nan 0.0100 0.0002
## 420 0.5501 nan 0.0100 0.0001
## 440 0.5418 nan 0.0100 0.0000
## 460 0.5333 nan 0.0100 -0.0001
## 480 0.5252 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3016 nan 0.0100 0.0042
## 3 1.2931 nan 0.0100 0.0039
## 4 1.2844 nan 0.0100 0.0039
## 5 1.2756 nan 0.0100 0.0043
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2587 nan 0.0100 0.0040
## 8 1.2500 nan 0.0100 0.0035
## 9 1.2417 nan 0.0100 0.0034
## 10 1.2339 nan 0.0100 0.0033
## 20 1.1583 nan 0.0100 0.0034
## 40 1.0397 nan 0.0100 0.0025
## 60 0.9479 nan 0.0100 0.0018
## 80 0.8779 nan 0.0100 0.0013
## 100 0.8224 nan 0.0100 0.0007
## 120 0.7765 nan 0.0100 0.0005
## 140 0.7366 nan 0.0100 0.0004
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## 180 0.6769 nan 0.0100 0.0004
## 200 0.6525 nan 0.0100 0.0001
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## 240 0.6100 nan 0.0100 0.0002
## 260 0.5921 nan 0.0100 0.0001
## 280 0.5762 nan 0.0100 0.0002
## 300 0.5611 nan 0.0100 0.0001
## 320 0.5464 nan 0.0100 0.0001
## 340 0.5329 nan 0.0100 0.0000
## 360 0.5210 nan 0.0100 0.0000
## 380 0.5100 nan 0.0100 -0.0000
## 400 0.4990 nan 0.0100 -0.0001
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## 460 0.4694 nan 0.0100 -0.0001
## 480 0.4607 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0043
## 3 1.2932 nan 0.0100 0.0041
## 4 1.2843 nan 0.0100 0.0044
## 5 1.2752 nan 0.0100 0.0039
## 6 1.2672 nan 0.0100 0.0040
## 7 1.2589 nan 0.0100 0.0035
## 8 1.2511 nan 0.0100 0.0035
## 9 1.2428 nan 0.0100 0.0037
## 10 1.2345 nan 0.0100 0.0032
## 20 1.1602 nan 0.0100 0.0029
## 40 1.0420 nan 0.0100 0.0021
## 60 0.9519 nan 0.0100 0.0019
## 80 0.8819 nan 0.0100 0.0010
## 100 0.8250 nan 0.0100 0.0009
## 120 0.7786 nan 0.0100 0.0008
## 140 0.7401 nan 0.0100 0.0006
## 160 0.7096 nan 0.0100 0.0003
## 180 0.6804 nan 0.0100 0.0004
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## 260 0.5997 nan 0.0100 0.0001
## 280 0.5840 nan 0.0100 0.0000
## 300 0.5677 nan 0.0100 -0.0000
## 320 0.5537 nan 0.0100 0.0001
## 340 0.5415 nan 0.0100 -0.0002
## 360 0.5290 nan 0.0100 0.0001
## 380 0.5182 nan 0.0100 0.0001
## 400 0.5075 nan 0.0100 0.0000
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## 460 0.4777 nan 0.0100 -0.0002
## 480 0.4687 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3026 nan 0.0100 0.0045
## 3 1.2936 nan 0.0100 0.0038
## 4 1.2844 nan 0.0100 0.0038
## 5 1.2757 nan 0.0100 0.0040
## 6 1.2672 nan 0.0100 0.0038
## 7 1.2583 nan 0.0100 0.0040
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2423 nan 0.0100 0.0038
## 10 1.2340 nan 0.0100 0.0038
## 20 1.1616 nan 0.0100 0.0029
## 40 1.0460 nan 0.0100 0.0019
## 60 0.9583 nan 0.0100 0.0017
## 80 0.8877 nan 0.0100 0.0014
## 100 0.8331 nan 0.0100 0.0009
## 120 0.7865 nan 0.0100 0.0006
## 140 0.7500 nan 0.0100 0.0005
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## 180 0.6894 nan 0.0100 0.0004
## 200 0.6647 nan 0.0100 0.0002
## 220 0.6433 nan 0.0100 0.0003
## 240 0.6240 nan 0.0100 0.0002
## 260 0.6074 nan 0.0100 0.0002
## 280 0.5917 nan 0.0100 -0.0001
## 300 0.5776 nan 0.0100 0.0000
## 320 0.5631 nan 0.0100 -0.0000
## 340 0.5505 nan 0.0100 0.0000
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## 380 0.5268 nan 0.0100 -0.0001
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## 460 0.4874 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2921 nan 0.0100 0.0045
## 4 1.2826 nan 0.0100 0.0045
## 5 1.2728 nan 0.0100 0.0045
## 6 1.2636 nan 0.0100 0.0046
## 7 1.2542 nan 0.0100 0.0041
## 8 1.2460 nan 0.0100 0.0035
## 9 1.2378 nan 0.0100 0.0038
## 10 1.2294 nan 0.0100 0.0040
## 20 1.1529 nan 0.0100 0.0031
## 40 1.0303 nan 0.0100 0.0022
## 60 0.9361 nan 0.0100 0.0016
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## 100 0.8022 nan 0.0100 0.0010
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## 140 0.7131 nan 0.0100 0.0006
## 160 0.6779 nan 0.0100 0.0003
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## 260 0.5607 nan 0.0100 0.0000
## 280 0.5438 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0046
## 2 1.3018 nan 0.0100 0.0043
## 3 1.2918 nan 0.0100 0.0040
## 4 1.2829 nan 0.0100 0.0041
## 5 1.2736 nan 0.0100 0.0043
## 6 1.2645 nan 0.0100 0.0037
## 7 1.2561 nan 0.0100 0.0036
## 8 1.2475 nan 0.0100 0.0032
## 9 1.2387 nan 0.0100 0.0038
## 10 1.2299 nan 0.0100 0.0040
## 20 1.1550 nan 0.0100 0.0031
## 40 1.0315 nan 0.0100 0.0023
## 60 0.9367 nan 0.0100 0.0016
## 80 0.8641 nan 0.0100 0.0013
## 100 0.8061 nan 0.0100 0.0009
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## 140 0.7188 nan 0.0100 0.0006
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## 180 0.6535 nan 0.0100 0.0004
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## 240 0.5871 nan 0.0100 0.0001
## 260 0.5674 nan 0.0100 -0.0000
## 280 0.5500 nan 0.0100 0.0000
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## 460 0.4345 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0038
## 3 1.2936 nan 0.0100 0.0042
## 4 1.2852 nan 0.0100 0.0040
## 5 1.2763 nan 0.0100 0.0042
## 6 1.2675 nan 0.0100 0.0040
## 7 1.2589 nan 0.0100 0.0036
## 8 1.2500 nan 0.0100 0.0041
## 9 1.2419 nan 0.0100 0.0041
## 10 1.2341 nan 0.0100 0.0035
## 20 1.1581 nan 0.0100 0.0032
## 40 1.0362 nan 0.0100 0.0022
## 60 0.9449 nan 0.0100 0.0020
## 80 0.8733 nan 0.0100 0.0012
## 100 0.8161 nan 0.0100 0.0009
## 120 0.7689 nan 0.0100 0.0009
## 140 0.7289 nan 0.0100 0.0007
## 160 0.6948 nan 0.0100 0.0002
## 180 0.6669 nan 0.0100 0.0004
## 200 0.6403 nan 0.0100 0.0004
## 220 0.6171 nan 0.0100 0.0003
## 240 0.5962 nan 0.0100 0.0002
## 260 0.5772 nan 0.0100 0.0003
## 280 0.5599 nan 0.0100 -0.0000
## 300 0.5450 nan 0.0100 0.0001
## 320 0.5296 nan 0.0100 0.0001
## 340 0.5159 nan 0.0100 0.0001
## 360 0.5039 nan 0.0100 -0.0001
## 380 0.4915 nan 0.0100 -0.0001
## 400 0.4801 nan 0.0100 -0.0000
## 420 0.4690 nan 0.0100 -0.0002
## 440 0.4582 nan 0.0100 -0.0001
## 460 0.4475 nan 0.0100 -0.0001
## 480 0.4374 nan 0.0100 -0.0001
## 500 0.4271 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2314 nan 0.1000 0.0401
## 2 1.1605 nan 0.1000 0.0323
## 3 1.1031 nan 0.1000 0.0242
## 4 1.0488 nan 0.1000 0.0252
## 5 1.0018 nan 0.1000 0.0196
## 6 0.9617 nan 0.1000 0.0186
## 7 0.9263 nan 0.1000 0.0135
## 8 0.8936 nan 0.1000 0.0139
## 9 0.8667 nan 0.1000 0.0114
## 10 0.8392 nan 0.1000 0.0089
## 20 0.6774 nan 0.1000 0.0022
## 40 0.5462 nan 0.1000 0.0003
## 60 0.4580 nan 0.1000 -0.0003
## 80 0.3982 nan 0.1000 -0.0000
## 100 0.3466 nan 0.1000 -0.0003
## 120 0.3086 nan 0.1000 -0.0009
## 140 0.2725 nan 0.1000 -0.0003
## 160 0.2432 nan 0.1000 -0.0003
## 180 0.2205 nan 0.1000 0.0002
## 200 0.1967 nan 0.1000 -0.0001
## 220 0.1791 nan 0.1000 -0.0004
## 240 0.1616 nan 0.1000 -0.0001
## 260 0.1459 nan 0.1000 -0.0002
## 280 0.1338 nan 0.1000 -0.0004
## 300 0.1208 nan 0.1000 -0.0002
## 320 0.1120 nan 0.1000 -0.0002
## 340 0.1023 nan 0.1000 -0.0000
## 360 0.0937 nan 0.1000 -0.0003
## 380 0.0851 nan 0.1000 -0.0002
## 400 0.0779 nan 0.1000 -0.0002
## 420 0.0720 nan 0.1000 -0.0003
## 440 0.0660 nan 0.1000 -0.0000
## 460 0.0604 nan 0.1000 -0.0003
## 480 0.0557 nan 0.1000 -0.0001
## 500 0.0506 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0349
## 2 1.1680 nan 0.1000 0.0354
## 3 1.1105 nan 0.1000 0.0243
## 4 1.0523 nan 0.1000 0.0215
## 5 1.0117 nan 0.1000 0.0157
## 6 0.9685 nan 0.1000 0.0194
## 7 0.9385 nan 0.1000 0.0125
## 8 0.9045 nan 0.1000 0.0151
## 9 0.8763 nan 0.1000 0.0117
## 10 0.8474 nan 0.1000 0.0116
## 20 0.6930 nan 0.1000 0.0045
## 40 0.5567 nan 0.1000 0.0004
## 60 0.4782 nan 0.1000 -0.0009
## 80 0.4172 nan 0.1000 -0.0014
## 100 0.3676 nan 0.1000 -0.0001
## 120 0.3307 nan 0.1000 -0.0001
## 140 0.2938 nan 0.1000 -0.0009
## 160 0.2598 nan 0.1000 0.0000
## 180 0.2334 nan 0.1000 -0.0009
## 200 0.2072 nan 0.1000 -0.0003
## 220 0.1873 nan 0.1000 -0.0013
## 240 0.1692 nan 0.1000 -0.0001
## 260 0.1542 nan 0.1000 -0.0001
## 280 0.1390 nan 0.1000 -0.0006
## 300 0.1258 nan 0.1000 -0.0001
## 320 0.1147 nan 0.1000 -0.0002
## 340 0.1043 nan 0.1000 -0.0003
## 360 0.0959 nan 0.1000 -0.0000
## 380 0.0873 nan 0.1000 -0.0004
## 400 0.0795 nan 0.1000 -0.0003
## 420 0.0733 nan 0.1000 -0.0002
## 440 0.0674 nan 0.1000 -0.0003
## 460 0.0619 nan 0.1000 -0.0002
## 480 0.0560 nan 0.1000 -0.0001
## 500 0.0513 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2336 nan 0.1000 0.0349
## 2 1.1558 nan 0.1000 0.0339
## 3 1.0978 nan 0.1000 0.0240
## 4 1.0493 nan 0.1000 0.0195
## 5 1.0087 nan 0.1000 0.0154
## 6 0.9714 nan 0.1000 0.0175
## 7 0.9326 nan 0.1000 0.0157
## 8 0.9046 nan 0.1000 0.0123
## 9 0.8758 nan 0.1000 0.0113
## 10 0.8501 nan 0.1000 0.0104
## 20 0.7014 nan 0.1000 0.0038
## 40 0.5587 nan 0.1000 0.0008
## 60 0.4790 nan 0.1000 0.0004
## 80 0.4171 nan 0.1000 -0.0008
## 100 0.3684 nan 0.1000 -0.0001
## 120 0.3256 nan 0.1000 -0.0015
## 140 0.2939 nan 0.1000 -0.0008
## 160 0.2601 nan 0.1000 -0.0003
## 180 0.2312 nan 0.1000 -0.0003
## 200 0.2076 nan 0.1000 -0.0005
## 220 0.1886 nan 0.1000 -0.0004
## 240 0.1730 nan 0.1000 -0.0004
## 260 0.1569 nan 0.1000 -0.0008
## 280 0.1435 nan 0.1000 -0.0007
## 300 0.1303 nan 0.1000 -0.0006
## 320 0.1187 nan 0.1000 -0.0005
## 340 0.1093 nan 0.1000 -0.0003
## 360 0.1001 nan 0.1000 -0.0003
## 380 0.0922 nan 0.1000 -0.0002
## 400 0.0849 nan 0.1000 -0.0002
## 420 0.0781 nan 0.1000 -0.0002
## 440 0.0717 nan 0.1000 -0.0002
## 460 0.0663 nan 0.1000 -0.0004
## 480 0.0614 nan 0.1000 -0.0001
## 500 0.0566 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0441
## 2 1.1591 nan 0.1000 0.0300
## 3 1.0945 nan 0.1000 0.0265
## 4 1.0439 nan 0.1000 0.0209
## 5 0.9944 nan 0.1000 0.0233
## 6 0.9463 nan 0.1000 0.0190
## 7 0.9109 nan 0.1000 0.0123
## 8 0.8783 nan 0.1000 0.0113
## 9 0.8456 nan 0.1000 0.0118
## 10 0.8213 nan 0.1000 0.0096
## 20 0.6540 nan 0.1000 0.0022
## 40 0.4973 nan 0.1000 -0.0003
## 60 0.4093 nan 0.1000 -0.0005
## 80 0.3391 nan 0.1000 -0.0015
## 100 0.2866 nan 0.1000 -0.0008
## 120 0.2459 nan 0.1000 -0.0002
## 140 0.2135 nan 0.1000 -0.0005
## 160 0.1880 nan 0.1000 0.0002
## 180 0.1627 nan 0.1000 -0.0002
## 200 0.1402 nan 0.1000 -0.0003
## 220 0.1248 nan 0.1000 -0.0003
## 240 0.1107 nan 0.1000 -0.0002
## 260 0.0993 nan 0.1000 0.0000
## 280 0.0884 nan 0.1000 -0.0003
## 300 0.0795 nan 0.1000 -0.0005
## 320 0.0701 nan 0.1000 -0.0001
## 340 0.0621 nan 0.1000 -0.0002
## 360 0.0553 nan 0.1000 -0.0000
## 380 0.0505 nan 0.1000 -0.0001
## 400 0.0453 nan 0.1000 -0.0000
## 420 0.0410 nan 0.1000 -0.0001
## 440 0.0373 nan 0.1000 -0.0002
## 460 0.0332 nan 0.1000 0.0000
## 480 0.0300 nan 0.1000 -0.0001
## 500 0.0271 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2313 nan 0.1000 0.0417
## 2 1.1568 nan 0.1000 0.0332
## 3 1.0923 nan 0.1000 0.0304
## 4 1.0340 nan 0.1000 0.0249
## 5 0.9843 nan 0.1000 0.0211
## 6 0.9444 nan 0.1000 0.0157
## 7 0.9097 nan 0.1000 0.0153
## 8 0.8808 nan 0.1000 0.0118
## 9 0.8523 nan 0.1000 0.0091
## 10 0.8277 nan 0.1000 0.0104
## 20 0.6610 nan 0.1000 0.0016
## 40 0.5180 nan 0.1000 0.0002
## 60 0.4212 nan 0.1000 0.0010
## 80 0.3612 nan 0.1000 -0.0008
## 100 0.3056 nan 0.1000 -0.0003
## 120 0.2656 nan 0.1000 -0.0010
## 140 0.2330 nan 0.1000 -0.0013
## 160 0.2003 nan 0.1000 -0.0001
## 180 0.1734 nan 0.1000 -0.0000
## 200 0.1547 nan 0.1000 -0.0004
## 220 0.1369 nan 0.1000 -0.0005
## 240 0.1217 nan 0.1000 -0.0002
## 260 0.1093 nan 0.1000 -0.0001
## 280 0.0985 nan 0.1000 -0.0004
## 300 0.0863 nan 0.1000 -0.0001
## 320 0.0769 nan 0.1000 -0.0005
## 340 0.0683 nan 0.1000 -0.0003
## 360 0.0613 nan 0.1000 -0.0002
## 380 0.0553 nan 0.1000 -0.0003
## 400 0.0490 nan 0.1000 -0.0002
## 420 0.0439 nan 0.1000 -0.0001
## 440 0.0398 nan 0.1000 -0.0001
## 460 0.0364 nan 0.1000 -0.0001
## 480 0.0329 nan 0.1000 -0.0002
## 500 0.0295 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0414
## 2 1.1618 nan 0.1000 0.0299
## 3 1.0965 nan 0.1000 0.0290
## 4 1.0419 nan 0.1000 0.0210
## 5 0.9944 nan 0.1000 0.0208
## 6 0.9507 nan 0.1000 0.0194
## 7 0.9141 nan 0.1000 0.0150
## 8 0.8766 nan 0.1000 0.0151
## 9 0.8505 nan 0.1000 0.0088
## 10 0.8218 nan 0.1000 0.0113
## 20 0.6629 nan 0.1000 0.0031
## 40 0.5239 nan 0.1000 0.0025
## 60 0.4436 nan 0.1000 -0.0020
## 80 0.3738 nan 0.1000 -0.0006
## 100 0.3177 nan 0.1000 -0.0018
## 120 0.2717 nan 0.1000 -0.0015
## 140 0.2354 nan 0.1000 -0.0003
## 160 0.2092 nan 0.1000 -0.0010
## 180 0.1881 nan 0.1000 -0.0007
## 200 0.1647 nan 0.1000 -0.0004
## 220 0.1432 nan 0.1000 -0.0008
## 240 0.1275 nan 0.1000 -0.0003
## 260 0.1140 nan 0.1000 -0.0007
## 280 0.1014 nan 0.1000 -0.0002
## 300 0.0896 nan 0.1000 -0.0001
## 320 0.0804 nan 0.1000 -0.0003
## 340 0.0719 nan 0.1000 -0.0002
## 360 0.0650 nan 0.1000 -0.0003
## 380 0.0589 nan 0.1000 -0.0004
## 400 0.0535 nan 0.1000 -0.0002
## 420 0.0478 nan 0.1000 -0.0001
## 440 0.0434 nan 0.1000 -0.0001
## 460 0.0391 nan 0.1000 -0.0000
## 480 0.0354 nan 0.1000 -0.0001
## 500 0.0322 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2213 nan 0.1000 0.0432
## 2 1.1463 nan 0.1000 0.0338
## 3 1.0837 nan 0.1000 0.0267
## 4 1.0260 nan 0.1000 0.0244
## 5 0.9761 nan 0.1000 0.0220
## 6 0.9334 nan 0.1000 0.0194
## 7 0.8910 nan 0.1000 0.0183
## 8 0.8577 nan 0.1000 0.0122
## 9 0.8298 nan 0.1000 0.0097
## 10 0.8001 nan 0.1000 0.0101
## 20 0.6191 nan 0.1000 0.0023
## 40 0.4543 nan 0.1000 -0.0010
## 60 0.3611 nan 0.1000 0.0008
## 80 0.2914 nan 0.1000 0.0000
## 100 0.2420 nan 0.1000 -0.0011
## 120 0.2048 nan 0.1000 -0.0009
## 140 0.1749 nan 0.1000 -0.0009
## 160 0.1509 nan 0.1000 -0.0001
## 180 0.1307 nan 0.1000 -0.0003
## 200 0.1113 nan 0.1000 -0.0001
## 220 0.0967 nan 0.1000 -0.0001
## 240 0.0839 nan 0.1000 -0.0002
## 260 0.0736 nan 0.1000 -0.0001
## 280 0.0648 nan 0.1000 -0.0002
## 300 0.0562 nan 0.1000 -0.0001
## 320 0.0498 nan 0.1000 -0.0001
## 340 0.0432 nan 0.1000 -0.0002
## 360 0.0375 nan 0.1000 -0.0001
## 380 0.0334 nan 0.1000 -0.0002
## 400 0.0294 nan 0.1000 -0.0001
## 420 0.0255 nan 0.1000 -0.0000
## 440 0.0225 nan 0.1000 -0.0000
## 460 0.0200 nan 0.1000 -0.0000
## 480 0.0176 nan 0.1000 -0.0000
## 500 0.0155 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2252 nan 0.1000 0.0409
## 2 1.1526 nan 0.1000 0.0307
## 3 1.0827 nan 0.1000 0.0349
## 4 1.0284 nan 0.1000 0.0260
## 5 0.9807 nan 0.1000 0.0174
## 6 0.9402 nan 0.1000 0.0180
## 7 0.9021 nan 0.1000 0.0154
## 8 0.8692 nan 0.1000 0.0139
## 9 0.8398 nan 0.1000 0.0121
## 10 0.8081 nan 0.1000 0.0132
## 20 0.6367 nan 0.1000 0.0015
## 40 0.4728 nan 0.1000 -0.0007
## 60 0.3807 nan 0.1000 0.0008
## 80 0.3123 nan 0.1000 -0.0015
## 100 0.2593 nan 0.1000 0.0004
## 120 0.2187 nan 0.1000 -0.0012
## 140 0.1828 nan 0.1000 -0.0002
## 160 0.1556 nan 0.1000 -0.0001
## 180 0.1335 nan 0.1000 -0.0007
## 200 0.1133 nan 0.1000 -0.0001
## 220 0.0977 nan 0.1000 -0.0003
## 240 0.0842 nan 0.1000 -0.0002
## 260 0.0729 nan 0.1000 -0.0001
## 280 0.0635 nan 0.1000 -0.0002
## 300 0.0551 nan 0.1000 -0.0003
## 320 0.0483 nan 0.1000 -0.0002
## 340 0.0417 nan 0.1000 -0.0002
## 360 0.0366 nan 0.1000 -0.0000
## 380 0.0322 nan 0.1000 -0.0000
## 400 0.0279 nan 0.1000 -0.0001
## 420 0.0244 nan 0.1000 -0.0000
## 440 0.0213 nan 0.1000 -0.0001
## 460 0.0186 nan 0.1000 -0.0001
## 480 0.0162 nan 0.1000 -0.0000
## 500 0.0141 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2274 nan 0.1000 0.0426
## 2 1.1597 nan 0.1000 0.0303
## 3 1.0938 nan 0.1000 0.0281
## 4 1.0353 nan 0.1000 0.0258
## 5 0.9823 nan 0.1000 0.0193
## 6 0.9430 nan 0.1000 0.0175
## 7 0.9051 nan 0.1000 0.0159
## 8 0.8715 nan 0.1000 0.0141
## 9 0.8385 nan 0.1000 0.0122
## 10 0.8134 nan 0.1000 0.0096
## 20 0.6417 nan 0.1000 0.0017
## 40 0.4875 nan 0.1000 -0.0005
## 60 0.3988 nan 0.1000 -0.0018
## 80 0.3284 nan 0.1000 -0.0001
## 100 0.2746 nan 0.1000 -0.0002
## 120 0.2292 nan 0.1000 -0.0008
## 140 0.1931 nan 0.1000 -0.0002
## 160 0.1646 nan 0.1000 -0.0005
## 180 0.1428 nan 0.1000 -0.0004
## 200 0.1225 nan 0.1000 -0.0002
## 220 0.1072 nan 0.1000 -0.0007
## 240 0.0935 nan 0.1000 -0.0003
## 260 0.0814 nan 0.1000 -0.0002
## 280 0.0708 nan 0.1000 -0.0003
## 300 0.0632 nan 0.1000 -0.0001
## 320 0.0556 nan 0.1000 -0.0003
## 340 0.0490 nan 0.1000 0.0000
## 360 0.0434 nan 0.1000 -0.0001
## 380 0.0381 nan 0.1000 -0.0001
## 400 0.0334 nan 0.1000 -0.0001
## 420 0.0293 nan 0.1000 -0.0000
## 440 0.0259 nan 0.1000 -0.0000
## 460 0.0224 nan 0.1000 -0.0000
## 480 0.0199 nan 0.1000 -0.0000
## 500 0.0175 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2889 nan 0.0010 0.0003
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2586 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2038 nan 0.0010 0.0003
## 180 1.1910 nan 0.0010 0.0003
## 200 1.1786 nan 0.0010 0.0002
## 220 1.1665 nan 0.0010 0.0002
## 240 1.1549 nan 0.0010 0.0002
## 260 1.1439 nan 0.0010 0.0002
## 280 1.1329 nan 0.0010 0.0002
## 300 1.1222 nan 0.0010 0.0002
## 320 1.1120 nan 0.0010 0.0002
## 340 1.1019 nan 0.0010 0.0002
## 360 1.0920 nan 0.0010 0.0002
## 380 1.0826 nan 0.0010 0.0002
## 400 1.0734 nan 0.0010 0.0002
## 420 1.0643 nan 0.0010 0.0002
## 440 1.0556 nan 0.0010 0.0002
## 460 1.0470 nan 0.0010 0.0002
## 480 1.0387 nan 0.0010 0.0002
## 500 1.0304 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0005
## 7 1.3148 nan 0.0010 0.0003
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2886 nan 0.0010 0.0003
## 60 1.2734 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0002
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1670 nan 0.0010 0.0003
## 240 1.1554 nan 0.0010 0.0003
## 260 1.1442 nan 0.0010 0.0003
## 280 1.1335 nan 0.0010 0.0002
## 300 1.1230 nan 0.0010 0.0002
## 320 1.1127 nan 0.0010 0.0002
## 340 1.1024 nan 0.0010 0.0002
## 360 1.0926 nan 0.0010 0.0002
## 380 1.0831 nan 0.0010 0.0002
## 400 1.0739 nan 0.0010 0.0002
## 420 1.0650 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0478 nan 0.0010 0.0001
## 480 1.0393 nan 0.0010 0.0002
## 500 1.0312 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0003
## 40 1.2884 nan 0.0010 0.0003
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2582 nan 0.0010 0.0003
## 100 1.2441 nan 0.0010 0.0002
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2042 nan 0.0010 0.0003
## 180 1.1915 nan 0.0010 0.0003
## 200 1.1794 nan 0.0010 0.0002
## 220 1.1676 nan 0.0010 0.0003
## 240 1.1561 nan 0.0010 0.0002
## 260 1.1451 nan 0.0010 0.0002
## 280 1.1342 nan 0.0010 0.0003
## 300 1.1237 nan 0.0010 0.0002
## 320 1.1135 nan 0.0010 0.0002
## 340 1.1035 nan 0.0010 0.0002
## 360 1.0938 nan 0.0010 0.0002
## 380 1.0844 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0660 nan 0.0010 0.0002
## 440 1.0573 nan 0.0010 0.0002
## 460 1.0487 nan 0.0010 0.0002
## 480 1.0405 nan 0.0010 0.0002
## 500 1.0324 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0003
## 100 1.2382 nan 0.0010 0.0003
## 120 1.2232 nan 0.0010 0.0004
## 140 1.2091 nan 0.0010 0.0003
## 160 1.1950 nan 0.0010 0.0003
## 180 1.1816 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1428 nan 0.0010 0.0003
## 260 1.1309 nan 0.0010 0.0003
## 280 1.1194 nan 0.0010 0.0003
## 300 1.1079 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0862 nan 0.0010 0.0002
## 360 1.0757 nan 0.0010 0.0002
## 380 1.0656 nan 0.0010 0.0002
## 400 1.0557 nan 0.0010 0.0002
## 420 1.0461 nan 0.0010 0.0002
## 440 1.0368 nan 0.0010 0.0001
## 460 1.0277 nan 0.0010 0.0001
## 480 1.0189 nan 0.0010 0.0002
## 500 1.0104 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0003
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0003
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2538 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2240 nan 0.0010 0.0004
## 140 1.2097 nan 0.0010 0.0003
## 160 1.1957 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1695 nan 0.0010 0.0003
## 220 1.1570 nan 0.0010 0.0002
## 240 1.1449 nan 0.0010 0.0003
## 260 1.1329 nan 0.0010 0.0003
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1105 nan 0.0010 0.0003
## 320 1.0995 nan 0.0010 0.0002
## 340 1.0888 nan 0.0010 0.0002
## 360 1.0785 nan 0.0010 0.0002
## 380 1.0686 nan 0.0010 0.0002
## 400 1.0590 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0001
## 440 1.0401 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0138 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0003
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2541 nan 0.0010 0.0004
## 100 1.2390 nan 0.0010 0.0004
## 120 1.2243 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1970 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1712 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0003
## 240 1.1466 nan 0.0010 0.0003
## 260 1.1348 nan 0.0010 0.0003
## 280 1.1233 nan 0.0010 0.0002
## 300 1.1123 nan 0.0010 0.0002
## 320 1.1015 nan 0.0010 0.0002
## 340 1.0910 nan 0.0010 0.0002
## 360 1.0811 nan 0.0010 0.0002
## 380 1.0711 nan 0.0010 0.0002
## 400 1.0613 nan 0.0010 0.0002
## 420 1.0518 nan 0.0010 0.0002
## 440 1.0426 nan 0.0010 0.0002
## 460 1.0334 nan 0.0010 0.0002
## 480 1.0249 nan 0.0010 0.0002
## 500 1.0163 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2501 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0003
## 120 1.2187 nan 0.0010 0.0004
## 140 1.2038 nan 0.0010 0.0004
## 160 1.1889 nan 0.0010 0.0003
## 180 1.1745 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1477 nan 0.0010 0.0003
## 240 1.1348 nan 0.0010 0.0003
## 260 1.1222 nan 0.0010 0.0003
## 280 1.1098 nan 0.0010 0.0003
## 300 1.0981 nan 0.0010 0.0003
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0758 nan 0.0010 0.0002
## 360 1.0651 nan 0.0010 0.0002
## 380 1.0546 nan 0.0010 0.0002
## 400 1.0442 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0149 nan 0.0010 0.0002
## 480 1.0058 nan 0.0010 0.0002
## 500 0.9970 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0003
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2343 nan 0.0010 0.0004
## 120 1.2189 nan 0.0010 0.0003
## 140 1.2037 nan 0.0010 0.0003
## 160 1.1891 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
## 200 1.1618 nan 0.0010 0.0003
## 220 1.1486 nan 0.0010 0.0003
## 240 1.1355 nan 0.0010 0.0003
## 260 1.1231 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0994 nan 0.0010 0.0003
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0771 nan 0.0010 0.0002
## 360 1.0663 nan 0.0010 0.0002
## 380 1.0558 nan 0.0010 0.0002
## 400 1.0454 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0256 nan 0.0010 0.0002
## 460 1.0166 nan 0.0010 0.0002
## 480 1.0076 nan 0.0010 0.0002
## 500 0.9987 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2069 nan 0.0010 0.0003
## 160 1.1923 nan 0.0010 0.0003
## 180 1.1783 nan 0.0010 0.0003
## 200 1.1647 nan 0.0010 0.0002
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1389 nan 0.0010 0.0003
## 260 1.1265 nan 0.0010 0.0003
## 280 1.1145 nan 0.0010 0.0003
## 300 1.1029 nan 0.0010 0.0003
## 320 1.0917 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0002
## 360 1.0701 nan 0.0010 0.0002
## 380 1.0596 nan 0.0010 0.0002
## 400 1.0494 nan 0.0010 0.0002
## 420 1.0399 nan 0.0010 0.0002
## 440 1.0305 nan 0.0010 0.0002
## 460 1.0214 nan 0.0010 0.0002
## 480 1.0124 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0035
## 2 1.3037 nan 0.0100 0.0042
## 3 1.2967 nan 0.0100 0.0031
## 4 1.2886 nan 0.0100 0.0037
## 5 1.2806 nan 0.0100 0.0034
## 6 1.2728 nan 0.0100 0.0035
## 7 1.2650 nan 0.0100 0.0033
## 8 1.2580 nan 0.0100 0.0029
## 9 1.2508 nan 0.0100 0.0032
## 10 1.2432 nan 0.0100 0.0036
## 20 1.1779 nan 0.0100 0.0029
## 40 1.0731 nan 0.0100 0.0020
## 60 0.9909 nan 0.0100 0.0016
## 80 0.9278 nan 0.0100 0.0012
## 100 0.8764 nan 0.0100 0.0008
## 120 0.8344 nan 0.0100 0.0004
## 140 0.7997 nan 0.0100 0.0004
## 160 0.7703 nan 0.0100 0.0000
## 180 0.7457 nan 0.0100 0.0003
## 200 0.7248 nan 0.0100 0.0002
## 220 0.7062 nan 0.0100 0.0000
## 240 0.6885 nan 0.0100 0.0001
## 260 0.6737 nan 0.0100 0.0001
## 280 0.6576 nan 0.0100 0.0001
## 300 0.6441 nan 0.0100 0.0001
## 320 0.6322 nan 0.0100 0.0000
## 340 0.6197 nan 0.0100 0.0001
## 360 0.6094 nan 0.0100 0.0000
## 380 0.5983 nan 0.0100 0.0000
## 400 0.5890 nan 0.0100 -0.0000
## 420 0.5796 nan 0.0100 -0.0001
## 440 0.5706 nan 0.0100 -0.0001
## 460 0.5618 nan 0.0100 -0.0001
## 480 0.5531 nan 0.0100 -0.0002
## 500 0.5451 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3041 nan 0.0100 0.0036
## 3 1.2960 nan 0.0100 0.0035
## 4 1.2879 nan 0.0100 0.0040
## 5 1.2799 nan 0.0100 0.0034
## 6 1.2729 nan 0.0100 0.0029
## 7 1.2653 nan 0.0100 0.0037
## 8 1.2576 nan 0.0100 0.0032
## 9 1.2501 nan 0.0100 0.0034
## 10 1.2428 nan 0.0100 0.0029
## 20 1.1771 nan 0.0100 0.0022
## 40 1.0737 nan 0.0100 0.0021
## 60 0.9929 nan 0.0100 0.0017
## 80 0.9297 nan 0.0100 0.0012
## 100 0.8790 nan 0.0100 0.0008
## 120 0.8368 nan 0.0100 0.0005
## 140 0.8034 nan 0.0100 0.0005
## 160 0.7749 nan 0.0100 0.0004
## 180 0.7491 nan 0.0100 0.0003
## 200 0.7280 nan 0.0100 0.0001
## 220 0.7097 nan 0.0100 -0.0000
## 240 0.6936 nan 0.0100 -0.0000
## 260 0.6784 nan 0.0100 -0.0000
## 280 0.6657 nan 0.0100 0.0000
## 300 0.6530 nan 0.0100 0.0002
## 320 0.6405 nan 0.0100 0.0001
## 340 0.6298 nan 0.0100 -0.0001
## 360 0.6193 nan 0.0100 0.0001
## 380 0.6095 nan 0.0100 -0.0000
## 400 0.5999 nan 0.0100 -0.0000
## 420 0.5904 nan 0.0100 -0.0001
## 440 0.5810 nan 0.0100 0.0001
## 460 0.5719 nan 0.0100 -0.0001
## 480 0.5631 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3047 nan 0.0100 0.0035
## 3 1.2967 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0038
## 5 1.2806 nan 0.0100 0.0039
## 6 1.2743 nan 0.0100 0.0031
## 7 1.2668 nan 0.0100 0.0030
## 8 1.2599 nan 0.0100 0.0032
## 9 1.2527 nan 0.0100 0.0032
## 10 1.2454 nan 0.0100 0.0031
## 20 1.1801 nan 0.0100 0.0029
## 40 1.0771 nan 0.0100 0.0021
## 60 0.9967 nan 0.0100 0.0015
## 80 0.9323 nan 0.0100 0.0011
## 100 0.8795 nan 0.0100 0.0008
## 120 0.8377 nan 0.0100 0.0005
## 140 0.8016 nan 0.0100 0.0004
## 160 0.7725 nan 0.0100 0.0005
## 180 0.7481 nan 0.0100 0.0003
## 200 0.7272 nan 0.0100 0.0002
## 220 0.7086 nan 0.0100 0.0001
## 240 0.6933 nan 0.0100 0.0001
## 260 0.6787 nan 0.0100 0.0000
## 280 0.6655 nan 0.0100 -0.0001
## 300 0.6541 nan 0.0100 0.0002
## 320 0.6415 nan 0.0100 0.0001
## 340 0.6311 nan 0.0100 0.0000
## 360 0.6213 nan 0.0100 0.0001
## 380 0.6105 nan 0.0100 -0.0001
## 400 0.6016 nan 0.0100 0.0000
## 420 0.5915 nan 0.0100 -0.0000
## 440 0.5824 nan 0.0100 -0.0001
## 460 0.5738 nan 0.0100 -0.0000
## 480 0.5664 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0040
## 2 1.3034 nan 0.0100 0.0041
## 3 1.2949 nan 0.0100 0.0040
## 4 1.2863 nan 0.0100 0.0039
## 5 1.2780 nan 0.0100 0.0037
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2627 nan 0.0100 0.0034
## 8 1.2549 nan 0.0100 0.0038
## 9 1.2473 nan 0.0100 0.0033
## 10 1.2396 nan 0.0100 0.0038
## 20 1.1698 nan 0.0100 0.0033
## 40 1.0584 nan 0.0100 0.0020
## 60 0.9733 nan 0.0100 0.0016
## 80 0.9061 nan 0.0100 0.0012
## 100 0.8521 nan 0.0100 0.0011
## 120 0.8075 nan 0.0100 0.0006
## 140 0.7708 nan 0.0100 0.0006
## 160 0.7394 nan 0.0100 0.0004
## 180 0.7132 nan 0.0100 0.0004
## 200 0.6910 nan 0.0100 0.0003
## 220 0.6700 nan 0.0100 0.0001
## 240 0.6509 nan 0.0100 0.0001
## 260 0.6335 nan 0.0100 0.0002
## 280 0.6182 nan 0.0100 0.0002
## 300 0.6026 nan 0.0100 0.0000
## 320 0.5886 nan 0.0100 -0.0001
## 340 0.5763 nan 0.0100 0.0000
## 360 0.5641 nan 0.0100 -0.0002
## 380 0.5536 nan 0.0100 -0.0001
## 400 0.5428 nan 0.0100 -0.0002
## 420 0.5320 nan 0.0100 0.0001
## 440 0.5211 nan 0.0100 0.0002
## 460 0.5100 nan 0.0100 -0.0001
## 480 0.5004 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3028 nan 0.0100 0.0041
## 3 1.2937 nan 0.0100 0.0040
## 4 1.2854 nan 0.0100 0.0037
## 5 1.2771 nan 0.0100 0.0032
## 6 1.2695 nan 0.0100 0.0035
## 7 1.2614 nan 0.0100 0.0036
## 8 1.2531 nan 0.0100 0.0038
## 9 1.2456 nan 0.0100 0.0037
## 10 1.2384 nan 0.0100 0.0028
## 20 1.1713 nan 0.0100 0.0028
## 40 1.0581 nan 0.0100 0.0022
## 60 0.9730 nan 0.0100 0.0015
## 80 0.9036 nan 0.0100 0.0011
## 100 0.8504 nan 0.0100 0.0007
## 120 0.8079 nan 0.0100 0.0006
## 140 0.7723 nan 0.0100 0.0005
## 160 0.7412 nan 0.0100 0.0005
## 180 0.7155 nan 0.0100 0.0005
## 200 0.6922 nan 0.0100 0.0003
## 220 0.6707 nan 0.0100 0.0001
## 240 0.6535 nan 0.0100 0.0002
## 260 0.6368 nan 0.0100 0.0001
## 280 0.6216 nan 0.0100 0.0002
## 300 0.6074 nan 0.0100 -0.0000
## 320 0.5943 nan 0.0100 -0.0002
## 340 0.5820 nan 0.0100 0.0001
## 360 0.5700 nan 0.0100 -0.0000
## 380 0.5595 nan 0.0100 -0.0001
## 400 0.5479 nan 0.0100 0.0000
## 420 0.5368 nan 0.0100 -0.0001
## 440 0.5264 nan 0.0100 -0.0001
## 460 0.5168 nan 0.0100 -0.0000
## 480 0.5068 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0035
## 2 1.3028 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2856 nan 0.0100 0.0038
## 5 1.2783 nan 0.0100 0.0031
## 6 1.2706 nan 0.0100 0.0035
## 7 1.2627 nan 0.0100 0.0036
## 8 1.2556 nan 0.0100 0.0034
## 9 1.2480 nan 0.0100 0.0033
## 10 1.2408 nan 0.0100 0.0026
## 20 1.1700 nan 0.0100 0.0032
## 40 1.0617 nan 0.0100 0.0021
## 60 0.9792 nan 0.0100 0.0011
## 80 0.9120 nan 0.0100 0.0011
## 100 0.8599 nan 0.0100 0.0008
## 120 0.8163 nan 0.0100 0.0009
## 140 0.7817 nan 0.0100 0.0004
## 160 0.7523 nan 0.0100 0.0003
## 180 0.7241 nan 0.0100 0.0005
## 200 0.7017 nan 0.0100 0.0002
## 220 0.6810 nan 0.0100 0.0001
## 240 0.6617 nan 0.0100 0.0002
## 260 0.6461 nan 0.0100 -0.0001
## 280 0.6321 nan 0.0100 0.0001
## 300 0.6178 nan 0.0100 0.0002
## 320 0.6062 nan 0.0100 0.0000
## 340 0.5932 nan 0.0100 0.0001
## 360 0.5807 nan 0.0100 0.0001
## 380 0.5697 nan 0.0100 -0.0001
## 400 0.5585 nan 0.0100 -0.0002
## 420 0.5473 nan 0.0100 -0.0001
## 440 0.5376 nan 0.0100 0.0000
## 460 0.5281 nan 0.0100 -0.0000
## 480 0.5189 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3025 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2845 nan 0.0100 0.0041
## 5 1.2754 nan 0.0100 0.0041
## 6 1.2668 nan 0.0100 0.0038
## 7 1.2586 nan 0.0100 0.0031
## 8 1.2493 nan 0.0100 0.0042
## 9 1.2416 nan 0.0100 0.0036
## 10 1.2341 nan 0.0100 0.0036
## 20 1.1629 nan 0.0100 0.0029
## 40 1.0458 nan 0.0100 0.0020
## 60 0.9554 nan 0.0100 0.0014
## 80 0.8841 nan 0.0100 0.0011
## 100 0.8301 nan 0.0100 0.0008
## 120 0.7839 nan 0.0100 0.0006
## 140 0.7460 nan 0.0100 0.0004
## 160 0.7117 nan 0.0100 0.0005
## 180 0.6832 nan 0.0100 0.0003
## 200 0.6586 nan 0.0100 0.0004
## 220 0.6358 nan 0.0100 0.0001
## 240 0.6163 nan 0.0100 0.0003
## 260 0.5979 nan 0.0100 -0.0001
## 280 0.5813 nan 0.0100 -0.0000
## 300 0.5658 nan 0.0100 0.0001
## 320 0.5501 nan 0.0100 0.0001
## 340 0.5372 nan 0.0100 -0.0001
## 360 0.5238 nan 0.0100 0.0002
## 380 0.5116 nan 0.0100 -0.0001
## 400 0.4989 nan 0.0100 0.0000
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## 440 0.4762 nan 0.0100 -0.0001
## 460 0.4659 nan 0.0100 -0.0000
## 480 0.4570 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0043
## 2 1.3018 nan 0.0100 0.0042
## 3 1.2935 nan 0.0100 0.0037
## 4 1.2844 nan 0.0100 0.0040
## 5 1.2753 nan 0.0100 0.0041
## 6 1.2667 nan 0.0100 0.0037
## 7 1.2588 nan 0.0100 0.0041
## 8 1.2510 nan 0.0100 0.0037
## 9 1.2423 nan 0.0100 0.0040
## 10 1.2339 nan 0.0100 0.0039
## 20 1.1605 nan 0.0100 0.0031
## 40 1.0445 nan 0.0100 0.0021
## 60 0.9549 nan 0.0100 0.0015
## 80 0.8857 nan 0.0100 0.0014
## 100 0.8286 nan 0.0100 0.0008
## 120 0.7838 nan 0.0100 0.0006
## 140 0.7463 nan 0.0100 0.0005
## 160 0.7140 nan 0.0100 0.0003
## 180 0.6853 nan 0.0100 0.0003
## 200 0.6618 nan 0.0100 0.0002
## 220 0.6400 nan 0.0100 0.0003
## 240 0.6200 nan 0.0100 0.0001
## 260 0.6029 nan 0.0100 0.0001
## 280 0.5868 nan 0.0100 -0.0001
## 300 0.5719 nan 0.0100 0.0001
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## 340 0.5454 nan 0.0100 0.0000
## 360 0.5324 nan 0.0100 -0.0000
## 380 0.5198 nan 0.0100 0.0001
## 400 0.5078 nan 0.0100 -0.0000
## 420 0.4967 nan 0.0100 0.0001
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## 460 0.4770 nan 0.0100 -0.0000
## 480 0.4675 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3026 nan 0.0100 0.0038
## 3 1.2945 nan 0.0100 0.0038
## 4 1.2854 nan 0.0100 0.0041
## 5 1.2769 nan 0.0100 0.0038
## 6 1.2684 nan 0.0100 0.0038
## 7 1.2604 nan 0.0100 0.0035
## 8 1.2530 nan 0.0100 0.0034
## 9 1.2448 nan 0.0100 0.0038
## 10 1.2363 nan 0.0100 0.0039
## 20 1.1636 nan 0.0100 0.0032
## 40 1.0503 nan 0.0100 0.0019
## 60 0.9626 nan 0.0100 0.0010
## 80 0.8935 nan 0.0100 0.0014
## 100 0.8392 nan 0.0100 0.0009
## 120 0.7943 nan 0.0100 0.0006
## 140 0.7574 nan 0.0100 0.0004
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## 180 0.6987 nan 0.0100 0.0003
## 200 0.6747 nan 0.0100 0.0001
## 220 0.6532 nan 0.0100 -0.0000
## 240 0.6342 nan 0.0100 -0.0001
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## 280 0.6002 nan 0.0100 0.0000
## 300 0.5845 nan 0.0100 0.0001
## 320 0.5715 nan 0.0100 0.0002
## 340 0.5584 nan 0.0100 -0.0002
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## 380 0.5340 nan 0.0100 0.0000
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## 460 0.4900 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0401
## 2 1.1729 nan 0.1000 0.0282
## 3 1.1151 nan 0.1000 0.0269
## 4 1.0683 nan 0.1000 0.0214
## 5 1.0295 nan 0.1000 0.0194
## 6 0.9887 nan 0.1000 0.0147
## 7 0.9573 nan 0.1000 0.0123
## 8 0.9280 nan 0.1000 0.0115
## 9 0.9019 nan 0.1000 0.0074
## 10 0.8770 nan 0.1000 0.0098
## 20 0.7268 nan 0.1000 0.0026
## 40 0.5977 nan 0.1000 -0.0007
## 60 0.5136 nan 0.1000 -0.0005
## 80 0.4465 nan 0.1000 -0.0005
## 100 0.3950 nan 0.1000 0.0009
## 120 0.3512 nan 0.1000 -0.0012
## 140 0.3122 nan 0.1000 -0.0004
## 160 0.2791 nan 0.1000 0.0001
## 180 0.2496 nan 0.1000 -0.0004
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## 240 0.1874 nan 0.1000 -0.0008
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## 340 0.1251 nan 0.1000 0.0001
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## 380 0.1057 nan 0.1000 -0.0001
## 400 0.0967 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0396
## 2 1.1715 nan 0.1000 0.0296
## 3 1.1201 nan 0.1000 0.0225
## 4 1.0728 nan 0.1000 0.0202
## 5 1.0336 nan 0.1000 0.0167
## 6 0.9942 nan 0.1000 0.0170
## 7 0.9602 nan 0.1000 0.0107
## 8 0.9294 nan 0.1000 0.0107
## 9 0.8992 nan 0.1000 0.0116
## 10 0.8719 nan 0.1000 0.0101
## 20 0.7256 nan 0.1000 0.0024
## 40 0.5916 nan 0.1000 0.0000
## 60 0.5171 nan 0.1000 -0.0012
## 80 0.4477 nan 0.1000 -0.0003
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## 120 0.3519 nan 0.1000 -0.0008
## 140 0.3196 nan 0.1000 -0.0000
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## 180 0.2604 nan 0.1000 -0.0004
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## 220 0.2190 nan 0.1000 -0.0006
## 240 0.1977 nan 0.1000 -0.0013
## 260 0.1806 nan 0.1000 -0.0003
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## 300 0.1504 nan 0.1000 -0.0004
## 320 0.1372 nan 0.1000 -0.0000
## 340 0.1255 nan 0.1000 -0.0003
## 360 0.1170 nan 0.1000 -0.0008
## 380 0.1085 nan 0.1000 -0.0004
## 400 0.0994 nan 0.1000 -0.0001
## 420 0.0910 nan 0.1000 -0.0004
## 440 0.0845 nan 0.1000 -0.0003
## 460 0.0785 nan 0.1000 -0.0000
## 480 0.0724 nan 0.1000 -0.0001
## 500 0.0666 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2431 nan 0.1000 0.0378
## 2 1.1782 nan 0.1000 0.0298
## 3 1.1216 nan 0.1000 0.0261
## 4 1.0745 nan 0.1000 0.0189
## 5 1.0298 nan 0.1000 0.0191
## 6 0.9929 nan 0.1000 0.0146
## 7 0.9631 nan 0.1000 0.0121
## 8 0.9307 nan 0.1000 0.0123
## 9 0.9023 nan 0.1000 0.0122
## 10 0.8818 nan 0.1000 0.0071
## 20 0.7278 nan 0.1000 0.0026
## 40 0.6070 nan 0.1000 -0.0006
## 60 0.5341 nan 0.1000 0.0005
## 80 0.4740 nan 0.1000 -0.0007
## 100 0.4226 nan 0.1000 -0.0003
## 120 0.3808 nan 0.1000 -0.0011
## 140 0.3447 nan 0.1000 -0.0006
## 160 0.3136 nan 0.1000 -0.0015
## 180 0.2837 nan 0.1000 -0.0013
## 200 0.2569 nan 0.1000 -0.0008
## 220 0.2336 nan 0.1000 -0.0002
## 240 0.2160 nan 0.1000 -0.0006
## 260 0.1945 nan 0.1000 -0.0001
## 280 0.1773 nan 0.1000 -0.0007
## 300 0.1629 nan 0.1000 -0.0007
## 320 0.1499 nan 0.1000 -0.0004
## 340 0.1378 nan 0.1000 -0.0002
## 360 0.1263 nan 0.1000 -0.0004
## 380 0.1164 nan 0.1000 -0.0002
## 400 0.1077 nan 0.1000 -0.0001
## 420 0.0987 nan 0.1000 -0.0002
## 440 0.0919 nan 0.1000 -0.0001
## 460 0.0857 nan 0.1000 -0.0004
## 480 0.0791 nan 0.1000 -0.0002
## 500 0.0730 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0384
## 2 1.1678 nan 0.1000 0.0297
## 3 1.1107 nan 0.1000 0.0258
## 4 1.0557 nan 0.1000 0.0220
## 5 1.0125 nan 0.1000 0.0161
## 6 0.9763 nan 0.1000 0.0147
## 7 0.9401 nan 0.1000 0.0152
## 8 0.9068 nan 0.1000 0.0130
## 9 0.8778 nan 0.1000 0.0101
## 10 0.8511 nan 0.1000 0.0116
## 20 0.6976 nan 0.1000 0.0019
## 40 0.5496 nan 0.1000 0.0004
## 60 0.4642 nan 0.1000 -0.0009
## 80 0.3916 nan 0.1000 -0.0016
## 100 0.3333 nan 0.1000 -0.0016
## 120 0.2878 nan 0.1000 0.0000
## 140 0.2527 nan 0.1000 -0.0013
## 160 0.2210 nan 0.1000 0.0000
## 180 0.1970 nan 0.1000 -0.0001
## 200 0.1731 nan 0.1000 -0.0002
## 220 0.1539 nan 0.1000 -0.0004
## 240 0.1379 nan 0.1000 -0.0003
## 260 0.1238 nan 0.1000 -0.0006
## 280 0.1101 nan 0.1000 -0.0003
## 300 0.0986 nan 0.1000 0.0000
## 320 0.0884 nan 0.1000 -0.0003
## 340 0.0794 nan 0.1000 -0.0002
## 360 0.0714 nan 0.1000 -0.0001
## 380 0.0647 nan 0.1000 -0.0002
## 400 0.0589 nan 0.1000 -0.0002
## 420 0.0535 nan 0.1000 -0.0002
## 440 0.0485 nan 0.1000 -0.0001
## 460 0.0437 nan 0.1000 -0.0001
## 480 0.0399 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0382
## 2 1.1642 nan 0.1000 0.0326
## 3 1.1065 nan 0.1000 0.0210
## 4 1.0566 nan 0.1000 0.0186
## 5 1.0150 nan 0.1000 0.0158
## 6 0.9727 nan 0.1000 0.0146
## 7 0.9390 nan 0.1000 0.0155
## 8 0.9113 nan 0.1000 0.0113
## 9 0.8829 nan 0.1000 0.0110
## 10 0.8586 nan 0.1000 0.0095
## 20 0.7028 nan 0.1000 0.0027
## 40 0.5604 nan 0.1000 0.0003
## 60 0.4822 nan 0.1000 -0.0004
## 80 0.4148 nan 0.1000 -0.0008
## 100 0.3607 nan 0.1000 -0.0006
## 120 0.3038 nan 0.1000 -0.0001
## 140 0.2657 nan 0.1000 -0.0005
## 160 0.2327 nan 0.1000 -0.0010
## 180 0.2045 nan 0.1000 -0.0008
## 200 0.1807 nan 0.1000 -0.0002
## 220 0.1590 nan 0.1000 -0.0009
## 240 0.1426 nan 0.1000 -0.0005
## 260 0.1277 nan 0.1000 -0.0006
## 280 0.1143 nan 0.1000 -0.0006
## 300 0.1015 nan 0.1000 -0.0003
## 320 0.0900 nan 0.1000 -0.0003
## 340 0.0813 nan 0.1000 -0.0001
## 360 0.0718 nan 0.1000 -0.0002
## 380 0.0651 nan 0.1000 -0.0002
## 400 0.0586 nan 0.1000 -0.0001
## 420 0.0535 nan 0.1000 -0.0002
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## 460 0.0436 nan 0.1000 -0.0001
## 480 0.0397 nan 0.1000 -0.0002
## 500 0.0362 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2396 nan 0.1000 0.0358
## 2 1.1789 nan 0.1000 0.0281
## 3 1.1174 nan 0.1000 0.0267
## 4 1.0672 nan 0.1000 0.0199
## 5 1.0220 nan 0.1000 0.0203
## 6 0.9810 nan 0.1000 0.0191
## 7 0.9399 nan 0.1000 0.0125
## 8 0.9087 nan 0.1000 0.0112
## 9 0.8845 nan 0.1000 0.0079
## 10 0.8552 nan 0.1000 0.0128
## 20 0.7056 nan 0.1000 0.0011
## 40 0.5704 nan 0.1000 -0.0011
## 60 0.4808 nan 0.1000 -0.0011
## 80 0.4099 nan 0.1000 -0.0005
## 100 0.3566 nan 0.1000 -0.0015
## 120 0.3099 nan 0.1000 0.0001
## 140 0.2724 nan 0.1000 -0.0014
## 160 0.2423 nan 0.1000 -0.0015
## 180 0.2183 nan 0.1000 -0.0011
## 200 0.1937 nan 0.1000 0.0001
## 220 0.1743 nan 0.1000 -0.0008
## 240 0.1567 nan 0.1000 -0.0004
## 260 0.1391 nan 0.1000 -0.0006
## 280 0.1241 nan 0.1000 -0.0005
## 300 0.1116 nan 0.1000 -0.0004
## 320 0.1019 nan 0.1000 -0.0003
## 340 0.0927 nan 0.1000 -0.0003
## 360 0.0836 nan 0.1000 -0.0004
## 380 0.0767 nan 0.1000 -0.0005
## 400 0.0694 nan 0.1000 -0.0001
## 420 0.0621 nan 0.1000 -0.0002
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## 460 0.0511 nan 0.1000 -0.0002
## 480 0.0463 nan 0.1000 -0.0001
## 500 0.0422 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0376
## 2 1.1594 nan 0.1000 0.0335
## 3 1.0906 nan 0.1000 0.0306
## 4 1.0344 nan 0.1000 0.0223
## 5 0.9807 nan 0.1000 0.0213
## 6 0.9393 nan 0.1000 0.0149
## 7 0.9021 nan 0.1000 0.0150
## 8 0.8742 nan 0.1000 0.0092
## 9 0.8472 nan 0.1000 0.0085
## 10 0.8208 nan 0.1000 0.0090
## 20 0.6673 nan 0.1000 0.0015
## 40 0.5124 nan 0.1000 0.0003
## 60 0.4152 nan 0.1000 -0.0015
## 80 0.3504 nan 0.1000 -0.0008
## 100 0.2922 nan 0.1000 0.0009
## 120 0.2450 nan 0.1000 0.0000
## 140 0.2089 nan 0.1000 -0.0005
## 160 0.1760 nan 0.1000 -0.0001
## 180 0.1519 nan 0.1000 -0.0006
## 200 0.1317 nan 0.1000 -0.0000
## 220 0.1145 nan 0.1000 -0.0002
## 240 0.0995 nan 0.1000 -0.0002
## 260 0.0869 nan 0.1000 -0.0003
## 280 0.0760 nan 0.1000 -0.0002
## 300 0.0673 nan 0.1000 0.0000
## 320 0.0598 nan 0.1000 -0.0002
## 340 0.0525 nan 0.1000 -0.0001
## 360 0.0456 nan 0.1000 -0.0001
## 380 0.0399 nan 0.1000 -0.0001
## 400 0.0351 nan 0.1000 -0.0000
## 420 0.0311 nan 0.1000 -0.0001
## 440 0.0280 nan 0.1000 -0.0001
## 460 0.0251 nan 0.1000 -0.0001
## 480 0.0224 nan 0.1000 -0.0001
## 500 0.0198 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2265 nan 0.1000 0.0401
## 2 1.1588 nan 0.1000 0.0316
## 3 1.0868 nan 0.1000 0.0272
## 4 1.0326 nan 0.1000 0.0219
## 5 0.9892 nan 0.1000 0.0173
## 6 0.9531 nan 0.1000 0.0183
## 7 0.9182 nan 0.1000 0.0127
## 8 0.8882 nan 0.1000 0.0104
## 9 0.8553 nan 0.1000 0.0131
## 10 0.8290 nan 0.1000 0.0100
## 20 0.6713 nan 0.1000 0.0020
## 40 0.5188 nan 0.1000 0.0012
## 60 0.4193 nan 0.1000 0.0000
## 80 0.3447 nan 0.1000 -0.0005
## 100 0.2882 nan 0.1000 -0.0015
## 120 0.2441 nan 0.1000 -0.0007
## 140 0.2083 nan 0.1000 -0.0003
## 160 0.1793 nan 0.1000 -0.0008
## 180 0.1549 nan 0.1000 -0.0006
## 200 0.1321 nan 0.1000 -0.0004
## 220 0.1174 nan 0.1000 -0.0004
## 240 0.1016 nan 0.1000 -0.0001
## 260 0.0893 nan 0.1000 -0.0004
## 280 0.0793 nan 0.1000 -0.0002
## 300 0.0700 nan 0.1000 -0.0005
## 320 0.0615 nan 0.1000 -0.0003
## 340 0.0544 nan 0.1000 -0.0001
## 360 0.0486 nan 0.1000 -0.0001
## 380 0.0429 nan 0.1000 0.0001
## 400 0.0381 nan 0.1000 -0.0001
## 420 0.0336 nan 0.1000 -0.0001
## 440 0.0300 nan 0.1000 -0.0002
## 460 0.0268 nan 0.1000 -0.0001
## 480 0.0238 nan 0.1000 -0.0000
## 500 0.0213 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0400
## 2 1.1610 nan 0.1000 0.0317
## 3 1.0991 nan 0.1000 0.0291
## 4 1.0479 nan 0.1000 0.0223
## 5 1.0003 nan 0.1000 0.0179
## 6 0.9550 nan 0.1000 0.0194
## 7 0.9231 nan 0.1000 0.0121
## 8 0.8910 nan 0.1000 0.0126
## 9 0.8598 nan 0.1000 0.0104
## 10 0.8330 nan 0.1000 0.0076
## 20 0.6760 nan 0.1000 0.0016
## 40 0.5283 nan 0.1000 -0.0007
## 60 0.4366 nan 0.1000 -0.0006
## 80 0.3673 nan 0.1000 -0.0011
## 100 0.3103 nan 0.1000 -0.0008
## 120 0.2665 nan 0.1000 -0.0008
## 140 0.2295 nan 0.1000 -0.0008
## 160 0.1961 nan 0.1000 -0.0001
## 180 0.1708 nan 0.1000 -0.0002
## 200 0.1488 nan 0.1000 -0.0003
## 220 0.1296 nan 0.1000 -0.0004
## 240 0.1131 nan 0.1000 -0.0003
## 260 0.1007 nan 0.1000 -0.0008
## 280 0.0867 nan 0.1000 -0.0003
## 300 0.0754 nan 0.1000 -0.0002
## 320 0.0672 nan 0.1000 -0.0001
## 340 0.0590 nan 0.1000 -0.0003
## 360 0.0518 nan 0.1000 -0.0001
## 380 0.0458 nan 0.1000 -0.0001
## 400 0.0409 nan 0.1000 -0.0002
## 420 0.0364 nan 0.1000 -0.0001
## 440 0.0330 nan 0.1000 -0.0001
## 460 0.0293 nan 0.1000 -0.0001
## 480 0.0262 nan 0.1000 -0.0001
## 500 0.0233 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0005
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2235 nan 0.0010 0.0003
## 140 1.2090 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0003
## 180 1.1818 nan 0.0010 0.0003
## 200 1.1687 nan 0.0010 0.0003
## 220 1.1561 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0003
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1208 nan 0.0010 0.0003
## 300 1.1098 nan 0.0010 0.0003
## 320 1.0990 nan 0.0010 0.0002
## 340 1.0885 nan 0.0010 0.0002
## 360 1.0782 nan 0.0010 0.0002
## 380 1.0681 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0495 nan 0.0010 0.0002
## 440 1.0404 nan 0.0010 0.0002
## 460 1.0316 nan 0.0010 0.0002
## 480 1.0232 nan 0.0010 0.0002
## 500 1.0148 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2540 nan 0.0010 0.0003
## 100 1.2393 nan 0.0010 0.0004
## 120 1.2249 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1575 nan 0.0010 0.0003
## 240 1.1453 nan 0.0010 0.0003
## 260 1.1338 nan 0.0010 0.0003
## 280 1.1221 nan 0.0010 0.0002
## 300 1.1109 nan 0.0010 0.0003
## 320 1.1001 nan 0.0010 0.0002
## 340 1.0897 nan 0.0010 0.0002
## 360 1.0797 nan 0.0010 0.0002
## 380 1.0696 nan 0.0010 0.0002
## 400 1.0601 nan 0.0010 0.0002
## 420 1.0506 nan 0.0010 0.0002
## 440 1.0416 nan 0.0010 0.0002
## 460 1.0328 nan 0.0010 0.0002
## 480 1.0244 nan 0.0010 0.0002
## 500 1.0160 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3194 nan 0.0010 0.0005
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2393 nan 0.0010 0.0004
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1711 nan 0.0010 0.0003
## 220 1.1586 nan 0.0010 0.0002
## 240 1.1467 nan 0.0010 0.0003
## 260 1.1350 nan 0.0010 0.0002
## 280 1.1238 nan 0.0010 0.0002
## 300 1.1128 nan 0.0010 0.0002
## 320 1.1023 nan 0.0010 0.0003
## 340 1.0918 nan 0.0010 0.0003
## 360 1.0817 nan 0.0010 0.0002
## 380 1.0719 nan 0.0010 0.0002
## 400 1.0625 nan 0.0010 0.0002
## 420 1.0532 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0355 nan 0.0010 0.0002
## 480 1.0271 nan 0.0010 0.0002
## 500 1.0188 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2337 nan 0.0010 0.0004
## 120 1.2183 nan 0.0010 0.0003
## 140 1.2030 nan 0.0010 0.0003
## 160 1.1884 nan 0.0010 0.0004
## 180 1.1741 nan 0.0010 0.0003
## 200 1.1606 nan 0.0010 0.0003
## 220 1.1477 nan 0.0010 0.0002
## 240 1.1351 nan 0.0010 0.0003
## 260 1.1227 nan 0.0010 0.0003
## 280 1.1106 nan 0.0010 0.0002
## 300 1.0989 nan 0.0010 0.0003
## 320 1.0875 nan 0.0010 0.0002
## 340 1.0765 nan 0.0010 0.0003
## 360 1.0657 nan 0.0010 0.0002
## 380 1.0556 nan 0.0010 0.0002
## 400 1.0455 nan 0.0010 0.0002
## 420 1.0356 nan 0.0010 0.0002
## 440 1.0260 nan 0.0010 0.0002
## 460 1.0166 nan 0.0010 0.0002
## 480 1.0077 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0003
## 120 1.2195 nan 0.0010 0.0003
## 140 1.2044 nan 0.0010 0.0004
## 160 1.1898 nan 0.0010 0.0003
## 180 1.1757 nan 0.0010 0.0003
## 200 1.1620 nan 0.0010 0.0003
## 220 1.1486 nan 0.0010 0.0003
## 240 1.1360 nan 0.0010 0.0003
## 260 1.1238 nan 0.0010 0.0003
## 280 1.1117 nan 0.0010 0.0003
## 300 1.1001 nan 0.0010 0.0003
## 320 1.0888 nan 0.0010 0.0002
## 340 1.0778 nan 0.0010 0.0002
## 360 1.0673 nan 0.0010 0.0002
## 380 1.0570 nan 0.0010 0.0003
## 400 1.0466 nan 0.0010 0.0002
## 420 1.0366 nan 0.0010 0.0002
## 440 1.0275 nan 0.0010 0.0002
## 460 1.0181 nan 0.0010 0.0002
## 480 1.0092 nan 0.0010 0.0002
## 500 1.0003 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2515 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2055 nan 0.0010 0.0004
## 160 1.1907 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
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## 220 1.1507 nan 0.0010 0.0003
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## 280 1.1139 nan 0.0010 0.0002
## 300 1.1024 nan 0.0010 0.0003
## 320 1.0913 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0002
## 360 1.0702 nan 0.0010 0.0003
## 380 1.0598 nan 0.0010 0.0002
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## 440 1.0306 nan 0.0010 0.0002
## 460 1.0216 nan 0.0010 0.0002
## 480 1.0125 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3181 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2827 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0003
## 100 1.2303 nan 0.0010 0.0004
## 120 1.2139 nan 0.0010 0.0003
## 140 1.1983 nan 0.0010 0.0004
## 160 1.1831 nan 0.0010 0.0003
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## 300 1.0898 nan 0.0010 0.0003
## 320 1.0780 nan 0.0010 0.0003
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## 380 1.0445 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
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## 10 1.3116 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2834 nan 0.0010 0.0004
## 60 1.2653 nan 0.0010 0.0004
## 80 1.2483 nan 0.0010 0.0004
## 100 1.2315 nan 0.0010 0.0003
## 120 1.2153 nan 0.0010 0.0004
## 140 1.1999 nan 0.0010 0.0004
## 160 1.1848 nan 0.0010 0.0003
## 180 1.1706 nan 0.0010 0.0003
## 200 1.1565 nan 0.0010 0.0003
## 220 1.1429 nan 0.0010 0.0003
## 240 1.1298 nan 0.0010 0.0003
## 260 1.1172 nan 0.0010 0.0003
## 280 1.1049 nan 0.0010 0.0003
## 300 1.0926 nan 0.0010 0.0002
## 320 1.0807 nan 0.0010 0.0003
## 340 1.0695 nan 0.0010 0.0002
## 360 1.0582 nan 0.0010 0.0002
## 380 1.0477 nan 0.0010 0.0002
## 400 1.0369 nan 0.0010 0.0003
## 420 1.0267 nan 0.0010 0.0002
## 440 1.0167 nan 0.0010 0.0002
## 460 1.0072 nan 0.0010 0.0002
## 480 0.9978 nan 0.0010 0.0002
## 500 0.9889 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2837 nan 0.0010 0.0004
## 60 1.2662 nan 0.0010 0.0004
## 80 1.2491 nan 0.0010 0.0004
## 100 1.2329 nan 0.0010 0.0004
## 120 1.2170 nan 0.0010 0.0004
## 140 1.2015 nan 0.0010 0.0003
## 160 1.1866 nan 0.0010 0.0003
## 180 1.1721 nan 0.0010 0.0003
## 200 1.1581 nan 0.0010 0.0003
## 220 1.1448 nan 0.0010 0.0003
## 240 1.1318 nan 0.0010 0.0003
## 260 1.1192 nan 0.0010 0.0002
## 280 1.1070 nan 0.0010 0.0003
## 300 1.0954 nan 0.0010 0.0002
## 320 1.0840 nan 0.0010 0.0002
## 340 1.0727 nan 0.0010 0.0002
## 360 1.0621 nan 0.0010 0.0002
## 380 1.0515 nan 0.0010 0.0002
## 400 1.0412 nan 0.0010 0.0002
## 420 1.0312 nan 0.0010 0.0002
## 440 1.0216 nan 0.0010 0.0002
## 460 1.0122 nan 0.0010 0.0002
## 480 1.0031 nan 0.0010 0.0002
## 500 0.9940 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3040 nan 0.0100 0.0033
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2860 nan 0.0100 0.0036
## 5 1.2770 nan 0.0100 0.0042
## 6 1.2686 nan 0.0100 0.0039
## 7 1.2610 nan 0.0100 0.0035
## 8 1.2536 nan 0.0100 0.0038
## 9 1.2461 nan 0.0100 0.0034
## 10 1.2385 nan 0.0100 0.0037
## 20 1.1692 nan 0.0100 0.0031
## 40 1.0587 nan 0.0100 0.0021
## 60 0.9767 nan 0.0100 0.0016
## 80 0.9117 nan 0.0100 0.0011
## 100 0.8619 nan 0.0100 0.0006
## 120 0.8197 nan 0.0100 0.0006
## 140 0.7844 nan 0.0100 0.0004
## 160 0.7542 nan 0.0100 0.0004
## 180 0.7301 nan 0.0100 0.0004
## 200 0.7076 nan 0.0100 0.0005
## 220 0.6885 nan 0.0100 0.0004
## 240 0.6715 nan 0.0100 0.0001
## 260 0.6558 nan 0.0100 0.0000
## 280 0.6415 nan 0.0100 0.0002
## 300 0.6290 nan 0.0100 -0.0000
## 320 0.6164 nan 0.0100 0.0002
## 340 0.6046 nan 0.0100 -0.0000
## 360 0.5940 nan 0.0100 0.0001
## 380 0.5837 nan 0.0100 -0.0001
## 400 0.5738 nan 0.0100 -0.0001
## 420 0.5646 nan 0.0100 0.0000
## 440 0.5558 nan 0.0100 -0.0000
## 460 0.5472 nan 0.0100 0.0001
## 480 0.5386 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0046
## 2 1.3036 nan 0.0100 0.0040
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2875 nan 0.0100 0.0042
## 5 1.2794 nan 0.0100 0.0034
## 6 1.2710 nan 0.0100 0.0038
## 7 1.2624 nan 0.0100 0.0038
## 8 1.2548 nan 0.0100 0.0038
## 9 1.2461 nan 0.0100 0.0037
## 10 1.2386 nan 0.0100 0.0036
## 20 1.1680 nan 0.0100 0.0029
## 40 1.0590 nan 0.0100 0.0023
## 60 0.9772 nan 0.0100 0.0014
## 80 0.9110 nan 0.0100 0.0012
## 100 0.8609 nan 0.0100 0.0007
## 120 0.8194 nan 0.0100 0.0005
## 140 0.7858 nan 0.0100 0.0003
## 160 0.7563 nan 0.0100 0.0006
## 180 0.7306 nan 0.0100 0.0001
## 200 0.7091 nan 0.0100 0.0002
## 220 0.6897 nan 0.0100 0.0003
## 240 0.6731 nan 0.0100 0.0001
## 260 0.6569 nan 0.0100 0.0001
## 280 0.6422 nan 0.0100 0.0001
## 300 0.6297 nan 0.0100 0.0001
## 320 0.6181 nan 0.0100 0.0001
## 340 0.6065 nan 0.0100 0.0001
## 360 0.5969 nan 0.0100 -0.0001
## 380 0.5868 nan 0.0100 -0.0002
## 400 0.5775 nan 0.0100 0.0001
## 420 0.5682 nan 0.0100 0.0000
## 440 0.5594 nan 0.0100 -0.0001
## 460 0.5516 nan 0.0100 -0.0000
## 480 0.5434 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0045
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2778 nan 0.0100 0.0037
## 6 1.2690 nan 0.0100 0.0040
## 7 1.2615 nan 0.0100 0.0036
## 8 1.2548 nan 0.0100 0.0032
## 9 1.2471 nan 0.0100 0.0034
## 10 1.2394 nan 0.0100 0.0034
## 20 1.1717 nan 0.0100 0.0029
## 40 1.0651 nan 0.0100 0.0016
## 60 0.9820 nan 0.0100 0.0012
## 80 0.9186 nan 0.0100 0.0014
## 100 0.8670 nan 0.0100 0.0010
## 120 0.8245 nan 0.0100 0.0006
## 140 0.7899 nan 0.0100 0.0006
## 160 0.7620 nan 0.0100 0.0005
## 180 0.7379 nan 0.0100 0.0003
## 200 0.7156 nan 0.0100 0.0004
## 220 0.6966 nan 0.0100 0.0001
## 240 0.6794 nan 0.0100 0.0001
## 260 0.6648 nan 0.0100 0.0001
## 280 0.6505 nan 0.0100 -0.0001
## 300 0.6372 nan 0.0100 0.0002
## 320 0.6235 nan 0.0100 0.0001
## 340 0.6121 nan 0.0100 -0.0000
## 360 0.6014 nan 0.0100 0.0002
## 380 0.5911 nan 0.0100 -0.0001
## 400 0.5819 nan 0.0100 0.0001
## 420 0.5726 nan 0.0100 0.0001
## 440 0.5634 nan 0.0100 -0.0001
## 460 0.5557 nan 0.0100 0.0000
## 480 0.5473 nan 0.0100 0.0001
## 500 0.5397 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0046
## 2 1.3014 nan 0.0100 0.0048
## 3 1.2918 nan 0.0100 0.0044
## 4 1.2833 nan 0.0100 0.0040
## 5 1.2749 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0038
## 7 1.2576 nan 0.0100 0.0040
## 8 1.2487 nan 0.0100 0.0038
## 9 1.2403 nan 0.0100 0.0038
## 10 1.2323 nan 0.0100 0.0036
## 20 1.1592 nan 0.0100 0.0032
## 40 1.0439 nan 0.0100 0.0020
## 60 0.9579 nan 0.0100 0.0016
## 80 0.8900 nan 0.0100 0.0011
## 100 0.8372 nan 0.0100 0.0009
## 120 0.7932 nan 0.0100 0.0009
## 140 0.7566 nan 0.0100 0.0005
## 160 0.7257 nan 0.0100 0.0005
## 180 0.7003 nan 0.0100 0.0001
## 200 0.6765 nan 0.0100 0.0004
## 220 0.6548 nan 0.0100 0.0003
## 240 0.6359 nan 0.0100 0.0001
## 260 0.6177 nan 0.0100 0.0002
## 280 0.6018 nan 0.0100 0.0001
## 300 0.5871 nan 0.0100 0.0000
## 320 0.5737 nan 0.0100 0.0000
## 340 0.5616 nan 0.0100 0.0000
## 360 0.5493 nan 0.0100 -0.0000
## 380 0.5374 nan 0.0100 -0.0000
## 400 0.5268 nan 0.0100 -0.0000
## 420 0.5170 nan 0.0100 -0.0001
## 440 0.5071 nan 0.0100 0.0001
## 460 0.4982 nan 0.0100 -0.0000
## 480 0.4883 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3028 nan 0.0100 0.0045
## 3 1.2932 nan 0.0100 0.0045
## 4 1.2850 nan 0.0100 0.0038
## 5 1.2759 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0038
## 7 1.2594 nan 0.0100 0.0041
## 8 1.2510 nan 0.0100 0.0039
## 9 1.2425 nan 0.0100 0.0040
## 10 1.2351 nan 0.0100 0.0032
## 20 1.1608 nan 0.0100 0.0030
## 40 1.0456 nan 0.0100 0.0018
## 60 0.9587 nan 0.0100 0.0015
## 80 0.8919 nan 0.0100 0.0014
## 100 0.8383 nan 0.0100 0.0009
## 120 0.7954 nan 0.0100 0.0007
## 140 0.7582 nan 0.0100 0.0007
## 160 0.7273 nan 0.0100 0.0004
## 180 0.7019 nan 0.0100 0.0002
## 200 0.6781 nan 0.0100 0.0003
## 220 0.6590 nan 0.0100 0.0002
## 240 0.6409 nan 0.0100 -0.0000
## 260 0.6245 nan 0.0100 0.0000
## 280 0.6095 nan 0.0100 0.0000
## 300 0.5947 nan 0.0100 0.0000
## 320 0.5821 nan 0.0100 0.0001
## 340 0.5690 nan 0.0100 -0.0000
## 360 0.5576 nan 0.0100 -0.0001
## 380 0.5462 nan 0.0100 -0.0001
## 400 0.5347 nan 0.0100 -0.0000
## 420 0.5245 nan 0.0100 -0.0001
## 440 0.5142 nan 0.0100 -0.0002
## 460 0.5040 nan 0.0100 -0.0001
## 480 0.4938 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0034
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2861 nan 0.0100 0.0036
## 5 1.2774 nan 0.0100 0.0043
## 6 1.2690 nan 0.0100 0.0042
## 7 1.2605 nan 0.0100 0.0041
## 8 1.2521 nan 0.0100 0.0038
## 9 1.2445 nan 0.0100 0.0031
## 10 1.2367 nan 0.0100 0.0036
## 20 1.1644 nan 0.0100 0.0033
## 40 1.0503 nan 0.0100 0.0023
## 60 0.9639 nan 0.0100 0.0017
## 80 0.8981 nan 0.0100 0.0012
## 100 0.8440 nan 0.0100 0.0011
## 120 0.8016 nan 0.0100 0.0006
## 140 0.7649 nan 0.0100 0.0004
## 160 0.7340 nan 0.0100 0.0004
## 180 0.7088 nan 0.0100 0.0002
## 200 0.6844 nan 0.0100 0.0002
## 220 0.6655 nan 0.0100 0.0001
## 240 0.6473 nan 0.0100 -0.0000
## 260 0.6305 nan 0.0100 0.0001
## 280 0.6157 nan 0.0100 0.0002
## 300 0.6005 nan 0.0100 -0.0000
## 320 0.5867 nan 0.0100 -0.0001
## 340 0.5752 nan 0.0100 0.0000
## 360 0.5634 nan 0.0100 -0.0001
## 380 0.5515 nan 0.0100 0.0000
## 400 0.5410 nan 0.0100 -0.0000
## 420 0.5303 nan 0.0100 0.0001
## 440 0.5210 nan 0.0100 0.0001
## 460 0.5121 nan 0.0100 -0.0001
## 480 0.5024 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0047
## 2 1.3020 nan 0.0100 0.0047
## 3 1.2927 nan 0.0100 0.0043
## 4 1.2833 nan 0.0100 0.0043
## 5 1.2747 nan 0.0100 0.0037
## 6 1.2650 nan 0.0100 0.0045
## 7 1.2558 nan 0.0100 0.0039
## 8 1.2482 nan 0.0100 0.0034
## 9 1.2389 nan 0.0100 0.0041
## 10 1.2308 nan 0.0100 0.0035
## 20 1.1535 nan 0.0100 0.0032
## 40 1.0325 nan 0.0100 0.0020
## 60 0.9402 nan 0.0100 0.0018
## 80 0.8687 nan 0.0100 0.0012
## 100 0.8111 nan 0.0100 0.0011
## 120 0.7633 nan 0.0100 0.0008
## 140 0.7247 nan 0.0100 0.0006
## 160 0.6909 nan 0.0100 0.0005
## 180 0.6629 nan 0.0100 0.0005
## 200 0.6384 nan 0.0100 0.0002
## 220 0.6170 nan 0.0100 0.0003
## 240 0.5969 nan 0.0100 -0.0000
## 260 0.5802 nan 0.0100 0.0002
## 280 0.5622 nan 0.0100 0.0001
## 300 0.5470 nan 0.0100 0.0001
## 320 0.5329 nan 0.0100 0.0001
## 340 0.5189 nan 0.0100 0.0000
## 360 0.5061 nan 0.0100 0.0001
## 380 0.4944 nan 0.0100 0.0000
## 400 0.4826 nan 0.0100 0.0000
## 420 0.4720 nan 0.0100 0.0000
## 440 0.4610 nan 0.0100 -0.0001
## 460 0.4509 nan 0.0100 -0.0000
## 480 0.4407 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0040
## 2 1.3033 nan 0.0100 0.0035
## 3 1.2934 nan 0.0100 0.0047
## 4 1.2845 nan 0.0100 0.0038
## 5 1.2750 nan 0.0100 0.0042
## 6 1.2661 nan 0.0100 0.0039
## 7 1.2559 nan 0.0100 0.0043
## 8 1.2473 nan 0.0100 0.0040
## 9 1.2389 nan 0.0100 0.0040
## 10 1.2309 nan 0.0100 0.0037
## 20 1.1554 nan 0.0100 0.0028
## 40 1.0370 nan 0.0100 0.0026
## 60 0.9488 nan 0.0100 0.0019
## 80 0.8780 nan 0.0100 0.0016
## 100 0.8217 nan 0.0100 0.0009
## 120 0.7750 nan 0.0100 0.0006
## 140 0.7369 nan 0.0100 0.0008
## 160 0.7039 nan 0.0100 0.0002
## 180 0.6760 nan 0.0100 0.0003
## 200 0.6518 nan 0.0100 0.0003
## 220 0.6301 nan 0.0100 0.0002
## 240 0.6096 nan 0.0100 -0.0001
## 260 0.5912 nan 0.0100 0.0001
## 280 0.5742 nan 0.0100 0.0000
## 300 0.5580 nan 0.0100 0.0000
## 320 0.5421 nan 0.0100 0.0000
## 340 0.5284 nan 0.0100 0.0000
## 360 0.5160 nan 0.0100 -0.0000
## 380 0.5032 nan 0.0100 0.0001
## 400 0.4917 nan 0.0100 0.0001
## 420 0.4808 nan 0.0100 -0.0001
## 440 0.4704 nan 0.0100 0.0000
## 460 0.4597 nan 0.0100 0.0001
## 480 0.4501 nan 0.0100 -0.0000
## 500 0.4391 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0044
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2933 nan 0.0100 0.0045
## 4 1.2842 nan 0.0100 0.0042
## 5 1.2754 nan 0.0100 0.0039
## 6 1.2662 nan 0.0100 0.0037
## 7 1.2573 nan 0.0100 0.0043
## 8 1.2483 nan 0.0100 0.0040
## 9 1.2395 nan 0.0100 0.0036
## 10 1.2311 nan 0.0100 0.0039
## 20 1.1562 nan 0.0100 0.0034
## 40 1.0399 nan 0.0100 0.0024
## 60 0.9510 nan 0.0100 0.0017
## 80 0.8818 nan 0.0100 0.0013
## 100 0.8256 nan 0.0100 0.0011
## 120 0.7812 nan 0.0100 0.0009
## 140 0.7440 nan 0.0100 0.0008
## 160 0.7105 nan 0.0100 0.0005
## 180 0.6821 nan 0.0100 0.0002
## 200 0.6574 nan 0.0100 0.0001
## 220 0.6354 nan 0.0100 0.0000
## 240 0.6158 nan 0.0100 0.0000
## 260 0.5980 nan 0.0100 0.0000
## 280 0.5808 nan 0.0100 0.0001
## 300 0.5662 nan 0.0100 -0.0000
## 320 0.5524 nan 0.0100 0.0001
## 340 0.5394 nan 0.0100 -0.0001
## 360 0.5270 nan 0.0100 -0.0001
## 380 0.5156 nan 0.0100 0.0000
## 400 0.5049 nan 0.0100 -0.0001
## 420 0.4944 nan 0.0100 -0.0002
## 440 0.4832 nan 0.0100 -0.0001
## 460 0.4729 nan 0.0100 -0.0003
## 480 0.4634 nan 0.0100 -0.0002
## 500 0.4533 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0391
## 2 1.1606 nan 0.1000 0.0317
## 3 1.0978 nan 0.1000 0.0281
## 4 1.0456 nan 0.1000 0.0233
## 5 1.0038 nan 0.1000 0.0177
## 6 0.9669 nan 0.1000 0.0171
## 7 0.9352 nan 0.1000 0.0116
## 8 0.9046 nan 0.1000 0.0111
## 9 0.8770 nan 0.1000 0.0095
## 10 0.8480 nan 0.1000 0.0126
## 20 0.7049 nan 0.1000 0.0005
## 40 0.5837 nan 0.1000 -0.0007
## 60 0.5097 nan 0.1000 -0.0007
## 80 0.4429 nan 0.1000 -0.0013
## 100 0.3870 nan 0.1000 0.0001
## 120 0.3438 nan 0.1000 -0.0009
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2791 nan 0.1000 0.0000
## 180 0.2534 nan 0.1000 -0.0011
## 200 0.2256 nan 0.1000 -0.0009
## 220 0.2045 nan 0.1000 -0.0009
## 240 0.1859 nan 0.1000 -0.0001
## 260 0.1669 nan 0.1000 -0.0002
## 280 0.1517 nan 0.1000 -0.0004
## 300 0.1372 nan 0.1000 -0.0003
## 320 0.1264 nan 0.1000 -0.0002
## 340 0.1167 nan 0.1000 -0.0002
## 360 0.1061 nan 0.1000 -0.0002
## 380 0.0975 nan 0.1000 -0.0003
## 400 0.0899 nan 0.1000 -0.0001
## 420 0.0829 nan 0.1000 -0.0003
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## 460 0.0712 nan 0.1000 -0.0002
## 480 0.0653 nan 0.1000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0399
## 2 1.1595 nan 0.1000 0.0325
## 3 1.1030 nan 0.1000 0.0269
## 4 1.0548 nan 0.1000 0.0230
## 5 1.0085 nan 0.1000 0.0225
## 6 0.9741 nan 0.1000 0.0146
## 7 0.9410 nan 0.1000 0.0144
## 8 0.9134 nan 0.1000 0.0087
## 9 0.8853 nan 0.1000 0.0112
## 10 0.8645 nan 0.1000 0.0084
## 20 0.7108 nan 0.1000 0.0033
## 40 0.5788 nan 0.1000 0.0001
## 60 0.4987 nan 0.1000 -0.0003
## 80 0.4349 nan 0.1000 -0.0011
## 100 0.3878 nan 0.1000 -0.0011
## 120 0.3422 nan 0.1000 -0.0004
## 140 0.3096 nan 0.1000 -0.0011
## 160 0.2807 nan 0.1000 0.0003
## 180 0.2535 nan 0.1000 -0.0007
## 200 0.2304 nan 0.1000 -0.0007
## 220 0.2068 nan 0.1000 -0.0003
## 240 0.1883 nan 0.1000 -0.0011
## 260 0.1717 nan 0.1000 -0.0003
## 280 0.1568 nan 0.1000 -0.0005
## 300 0.1434 nan 0.1000 -0.0004
## 320 0.1328 nan 0.1000 -0.0003
## 340 0.1241 nan 0.1000 -0.0001
## 360 0.1148 nan 0.1000 -0.0002
## 380 0.1060 nan 0.1000 -0.0003
## 400 0.0975 nan 0.1000 -0.0002
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## 460 0.0752 nan 0.1000 -0.0004
## 480 0.0691 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0387
## 2 1.1681 nan 0.1000 0.0315
## 3 1.1086 nan 0.1000 0.0276
## 4 1.0559 nan 0.1000 0.0227
## 5 1.0135 nan 0.1000 0.0195
## 6 0.9750 nan 0.1000 0.0155
## 7 0.9468 nan 0.1000 0.0091
## 8 0.9178 nan 0.1000 0.0130
## 9 0.8926 nan 0.1000 0.0113
## 10 0.8674 nan 0.1000 0.0093
## 20 0.7199 nan 0.1000 0.0020
## 40 0.5855 nan 0.1000 -0.0010
## 60 0.5058 nan 0.1000 -0.0014
## 80 0.4462 nan 0.1000 -0.0018
## 100 0.3944 nan 0.1000 -0.0012
## 120 0.3542 nan 0.1000 -0.0008
## 140 0.3202 nan 0.1000 -0.0009
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## 180 0.2625 nan 0.1000 -0.0004
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## 220 0.2244 nan 0.1000 -0.0010
## 240 0.2047 nan 0.1000 -0.0006
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## 280 0.1747 nan 0.1000 -0.0009
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## 320 0.1487 nan 0.1000 -0.0005
## 340 0.1354 nan 0.1000 -0.0007
## 360 0.1250 nan 0.1000 -0.0007
## 380 0.1162 nan 0.1000 -0.0003
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## 460 0.0863 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2326 nan 0.1000 0.0431
## 2 1.1602 nan 0.1000 0.0308
## 3 1.1046 nan 0.1000 0.0218
## 4 1.0478 nan 0.1000 0.0257
## 5 1.0046 nan 0.1000 0.0194
## 6 0.9625 nan 0.1000 0.0185
## 7 0.9257 nan 0.1000 0.0149
## 8 0.8919 nan 0.1000 0.0128
## 9 0.8662 nan 0.1000 0.0117
## 10 0.8390 nan 0.1000 0.0110
## 20 0.6817 nan 0.1000 -0.0007
## 40 0.5341 nan 0.1000 0.0010
## 60 0.4454 nan 0.1000 -0.0005
## 80 0.3794 nan 0.1000 -0.0002
## 100 0.3291 nan 0.1000 -0.0006
## 120 0.2870 nan 0.1000 -0.0005
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## 180 0.1928 nan 0.1000 -0.0002
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## 240 0.1371 nan 0.1000 -0.0008
## 260 0.1223 nan 0.1000 -0.0002
## 280 0.1098 nan 0.1000 -0.0002
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## 340 0.0804 nan 0.1000 -0.0000
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## 380 0.0661 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0352
## 2 1.1547 nan 0.1000 0.0362
## 3 1.0940 nan 0.1000 0.0279
## 4 1.0366 nan 0.1000 0.0246
## 5 0.9896 nan 0.1000 0.0187
## 6 0.9509 nan 0.1000 0.0166
## 7 0.9207 nan 0.1000 0.0122
## 8 0.8876 nan 0.1000 0.0124
## 9 0.8620 nan 0.1000 0.0083
## 10 0.8374 nan 0.1000 0.0098
## 20 0.6832 nan 0.1000 0.0026
## 40 0.5446 nan 0.1000 -0.0011
## 60 0.4563 nan 0.1000 -0.0006
## 80 0.3913 nan 0.1000 -0.0008
## 100 0.3390 nan 0.1000 -0.0005
## 120 0.2964 nan 0.1000 -0.0007
## 140 0.2608 nan 0.1000 -0.0007
## 160 0.2268 nan 0.1000 -0.0007
## 180 0.2007 nan 0.1000 -0.0009
## 200 0.1770 nan 0.1000 -0.0004
## 220 0.1558 nan 0.1000 -0.0002
## 240 0.1390 nan 0.1000 -0.0006
## 260 0.1236 nan 0.1000 -0.0010
## 280 0.1102 nan 0.1000 -0.0008
## 300 0.0994 nan 0.1000 -0.0005
## 320 0.0890 nan 0.1000 -0.0003
## 340 0.0795 nan 0.1000 -0.0002
## 360 0.0719 nan 0.1000 -0.0003
## 380 0.0643 nan 0.1000 -0.0004
## 400 0.0574 nan 0.1000 -0.0003
## 420 0.0526 nan 0.1000 -0.0003
## 440 0.0472 nan 0.1000 -0.0001
## 460 0.0427 nan 0.1000 -0.0000
## 480 0.0390 nan 0.1000 -0.0001
## 500 0.0351 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0440
## 2 1.1622 nan 0.1000 0.0343
## 3 1.0991 nan 0.1000 0.0256
## 4 1.0473 nan 0.1000 0.0222
## 5 1.0025 nan 0.1000 0.0196
## 6 0.9601 nan 0.1000 0.0173
## 7 0.9255 nan 0.1000 0.0157
## 8 0.8968 nan 0.1000 0.0102
## 9 0.8702 nan 0.1000 0.0097
## 10 0.8477 nan 0.1000 0.0060
## 20 0.6818 nan 0.1000 0.0040
## 40 0.5470 nan 0.1000 -0.0013
## 60 0.4629 nan 0.1000 -0.0014
## 80 0.4006 nan 0.1000 -0.0024
## 100 0.3492 nan 0.1000 -0.0012
## 120 0.3049 nan 0.1000 -0.0013
## 140 0.2705 nan 0.1000 -0.0007
## 160 0.2415 nan 0.1000 -0.0002
## 180 0.2169 nan 0.1000 -0.0008
## 200 0.1946 nan 0.1000 -0.0006
## 220 0.1722 nan 0.1000 -0.0009
## 240 0.1532 nan 0.1000 -0.0001
## 260 0.1364 nan 0.1000 -0.0002
## 280 0.1240 nan 0.1000 -0.0004
## 300 0.1113 nan 0.1000 -0.0003
## 320 0.1005 nan 0.1000 -0.0001
## 340 0.0902 nan 0.1000 -0.0003
## 360 0.0828 nan 0.1000 -0.0002
## 380 0.0759 nan 0.1000 -0.0004
## 400 0.0691 nan 0.1000 -0.0004
## 420 0.0620 nan 0.1000 -0.0002
## 440 0.0565 nan 0.1000 -0.0002
## 460 0.0522 nan 0.1000 -0.0001
## 480 0.0473 nan 0.1000 -0.0002
## 500 0.0435 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2293 nan 0.1000 0.0420
## 2 1.1573 nan 0.1000 0.0318
## 3 1.0855 nan 0.1000 0.0292
## 4 1.0321 nan 0.1000 0.0216
## 5 0.9887 nan 0.1000 0.0177
## 6 0.9443 nan 0.1000 0.0136
## 7 0.9071 nan 0.1000 0.0154
## 8 0.8746 nan 0.1000 0.0138
## 9 0.8457 nan 0.1000 0.0106
## 10 0.8195 nan 0.1000 0.0106
## 20 0.6463 nan 0.1000 0.0043
## 40 0.4960 nan 0.1000 -0.0024
## 60 0.3977 nan 0.1000 0.0001
## 80 0.3306 nan 0.1000 0.0012
## 100 0.2779 nan 0.1000 -0.0009
## 120 0.2350 nan 0.1000 -0.0004
## 140 0.2028 nan 0.1000 -0.0005
## 160 0.1751 nan 0.1000 -0.0005
## 180 0.1494 nan 0.1000 -0.0000
## 200 0.1296 nan 0.1000 -0.0003
## 220 0.1155 nan 0.1000 -0.0003
## 240 0.1019 nan 0.1000 -0.0002
## 260 0.0899 nan 0.1000 -0.0000
## 280 0.0789 nan 0.1000 -0.0005
## 300 0.0695 nan 0.1000 -0.0002
## 320 0.0606 nan 0.1000 -0.0003
## 340 0.0536 nan 0.1000 -0.0002
## 360 0.0465 nan 0.1000 -0.0001
## 380 0.0417 nan 0.1000 -0.0002
## 400 0.0366 nan 0.1000 -0.0000
## 420 0.0321 nan 0.1000 -0.0001
## 440 0.0282 nan 0.1000 -0.0001
## 460 0.0251 nan 0.1000 -0.0002
## 480 0.0225 nan 0.1000 -0.0000
## 500 0.0201 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2276 nan 0.1000 0.0409
## 2 1.1466 nan 0.1000 0.0324
## 3 1.0798 nan 0.1000 0.0314
## 4 1.0183 nan 0.1000 0.0278
## 5 0.9667 nan 0.1000 0.0212
## 6 0.9265 nan 0.1000 0.0152
## 7 0.8925 nan 0.1000 0.0155
## 8 0.8595 nan 0.1000 0.0140
## 9 0.8350 nan 0.1000 0.0076
## 10 0.8081 nan 0.1000 0.0093
## 20 0.6393 nan 0.1000 0.0024
## 40 0.4879 nan 0.1000 0.0008
## 60 0.3943 nan 0.1000 0.0005
## 80 0.3298 nan 0.1000 0.0004
## 100 0.2823 nan 0.1000 -0.0006
## 120 0.2415 nan 0.1000 -0.0008
## 140 0.2050 nan 0.1000 -0.0009
## 160 0.1751 nan 0.1000 -0.0007
## 180 0.1517 nan 0.1000 -0.0008
## 200 0.1305 nan 0.1000 -0.0001
## 220 0.1140 nan 0.1000 0.0001
## 240 0.1000 nan 0.1000 -0.0002
## 260 0.0879 nan 0.1000 -0.0003
## 280 0.0776 nan 0.1000 -0.0003
## 300 0.0673 nan 0.1000 -0.0002
## 320 0.0597 nan 0.1000 -0.0003
## 340 0.0531 nan 0.1000 -0.0003
## 360 0.0473 nan 0.1000 -0.0002
## 380 0.0416 nan 0.1000 -0.0001
## 400 0.0365 nan 0.1000 -0.0001
## 420 0.0320 nan 0.1000 -0.0001
## 440 0.0280 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0001
## 480 0.0219 nan 0.1000 -0.0001
## 500 0.0194 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0385
## 2 1.1584 nan 0.1000 0.0368
## 3 1.0991 nan 0.1000 0.0273
## 4 1.0466 nan 0.1000 0.0222
## 5 0.9926 nan 0.1000 0.0247
## 6 0.9464 nan 0.1000 0.0195
## 7 0.9062 nan 0.1000 0.0151
## 8 0.8731 nan 0.1000 0.0148
## 9 0.8407 nan 0.1000 0.0122
## 10 0.8173 nan 0.1000 0.0095
## 20 0.6617 nan 0.1000 0.0025
## 40 0.5077 nan 0.1000 -0.0001
## 60 0.4201 nan 0.1000 0.0006
## 80 0.3559 nan 0.1000 -0.0011
## 100 0.3013 nan 0.1000 -0.0011
## 120 0.2559 nan 0.1000 -0.0008
## 140 0.2232 nan 0.1000 -0.0000
## 160 0.1944 nan 0.1000 -0.0004
## 180 0.1702 nan 0.1000 -0.0008
## 200 0.1483 nan 0.1000 -0.0007
## 220 0.1276 nan 0.1000 -0.0007
## 240 0.1111 nan 0.1000 -0.0002
## 260 0.0993 nan 0.1000 -0.0004
## 280 0.0867 nan 0.1000 -0.0003
## 300 0.0761 nan 0.1000 -0.0002
## 320 0.0675 nan 0.1000 -0.0002
## 340 0.0598 nan 0.1000 -0.0002
## 360 0.0535 nan 0.1000 -0.0001
## 380 0.0476 nan 0.1000 -0.0003
## 400 0.0421 nan 0.1000 -0.0002
## 420 0.0371 nan 0.1000 -0.0002
## 440 0.0327 nan 0.1000 -0.0002
## 460 0.0292 nan 0.1000 -0.0001
## 480 0.0264 nan 0.1000 -0.0002
## 500 0.0234 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2237 nan 0.1000 0.0446
## 2 1.1491 nan 0.1000 0.0353
## 3 1.0814 nan 0.1000 0.0290
## 4 1.0278 nan 0.1000 0.0217
## 5 0.9859 nan 0.1000 0.0156
## 6 0.9456 nan 0.1000 0.0164
## 7 0.9088 nan 0.1000 0.0144
## 8 0.8745 nan 0.1000 0.0144
## 9 0.8434 nan 0.1000 0.0122
## 10 0.8206 nan 0.1000 0.0090
## 20 0.6571 nan 0.1000 0.0038
## 40 0.5150 nan 0.1000 -0.0013
## 60 0.4277 nan 0.1000 0.0004
## 80 0.3509 nan 0.1000 -0.0002
## 100 0.2923 nan 0.1000 0.0003
## 120 0.2494 nan 0.1000 -0.0002
## 140 0.2124 nan 0.1000 0.0002
## 160 0.1827 nan 0.1000 0.0003
## 180 0.1574 nan 0.1000 -0.0003
## 200 0.1391 nan 0.1000 -0.0002
## 220 0.1235 nan 0.1000 -0.0003
## 240 0.1105 nan 0.1000 -0.0001
## 260 0.0973 nan 0.1000 -0.0000
## 280 0.0851 nan 0.1000 -0.0001
## 300 0.0753 nan 0.1000 -0.0002
## 320 0.0673 nan 0.1000 -0.0001
## 340 0.0593 nan 0.1000 -0.0000
## 360 0.0528 nan 0.1000 -0.0001
## 380 0.0470 nan 0.1000 -0.0001
## 400 0.0418 nan 0.1000 -0.0001
## 420 0.0372 nan 0.1000 -0.0001
## 440 0.0334 nan 0.1000 0.0000
## 460 0.0297 nan 0.1000 -0.0001
## 480 0.0266 nan 0.1000 -0.0000
## 500 0.0237 nan 0.1000 -0.0000##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_GBM_Tune## Stochastic Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode ROC Sens Spec
## 0.001 4 5 0.8910798 0.5294118 0.9513928
## 0.001 4 10 0.8914021 0.5276471 0.9524424
## 0.001 4 15 0.8897511 0.5211765 0.9545294
## 0.001 5 5 0.8958001 0.5658824 0.9478871
## 0.001 5 10 0.8958208 0.5658824 0.9478932
## 0.001 5 15 0.8941702 0.5547059 0.9517346
## 0.001 6 5 0.9005384 0.5864706 0.9485828
## 0.001 6 10 0.8994397 0.5870588 0.9499832
## 0.001 6 15 0.8974198 0.5688235 0.9496384
## 0.010 4 5 0.9185276 0.7723529 0.9087231
## 0.010 4 10 0.9165290 0.7705882 0.9090740
## 0.010 4 15 0.9153323 0.7617647 0.9104683
## 0.010 5 5 0.9237008 0.7764706 0.9094279
## 0.010 5 10 0.9228961 0.7741176 0.9111701
## 0.010 5 15 0.9216394 0.7682353 0.9115271
## 0.010 6 5 0.9296301 0.7852941 0.9132662
## 0.010 6 10 0.9281216 0.7794118 0.9146606
## 0.010 6 15 0.9263270 0.7835294 0.9164119
## 0.100 4 5 0.9573128 0.8964706 0.9359847
## 0.100 4 10 0.9550397 0.8994118 0.9401892
## 0.100 4 15 0.9547391 0.8947059 0.9450801
## 0.100 5 5 0.9584462 0.8958824 0.9408879
## 0.100 5 10 0.9581581 0.8976471 0.9443753
## 0.100 5 15 0.9566970 0.8964706 0.9436949
## 0.100 6 5 0.9599595 0.8970588 0.9405370
## 0.100 6 10 0.9594183 0.8964706 0.9443905
## 0.100 6 15 0.9576763 0.8917647 0.9471762
##
## Tuning parameter 'n.trees' was held constant at a value of 500
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 500, interaction.depth =
## 6, shrinkage = 0.1 and n.minobsinnode = 5.MBS_GBM_Tune$finalModel## A gradient boosted model with bernoulli loss function.
## 500 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.MBS_GBM_Tune$results## shrinkage interaction.depth n.minobsinnode n.trees ROC Sens
## 1 0.001 4 5 500 0.8910798 0.5294118
## 2 0.001 4 10 500 0.8914021 0.5276471
## 3 0.001 4 15 500 0.8897511 0.5211765
## 10 0.010 4 5 500 0.9185276 0.7723529
## 11 0.010 4 10 500 0.9165290 0.7705882
## 12 0.010 4 15 500 0.9153323 0.7617647
## 19 0.100 4 5 500 0.9573128 0.8964706
## 20 0.100 4 10 500 0.9550397 0.8994118
## 21 0.100 4 15 500 0.9547391 0.8947059
## 4 0.001 5 5 500 0.8958001 0.5658824
## 5 0.001 5 10 500 0.8958208 0.5658824
## 6 0.001 5 15 500 0.8941702 0.5547059
## 13 0.010 5 5 500 0.9237008 0.7764706
## 14 0.010 5 10 500 0.9228961 0.7741176
## 15 0.010 5 15 500 0.9216394 0.7682353
## 22 0.100 5 5 500 0.9584462 0.8958824
## 23 0.100 5 10 500 0.9581581 0.8976471
## 24 0.100 5 15 500 0.9566970 0.8964706
## 7 0.001 6 5 500 0.9005384 0.5864706
## 8 0.001 6 10 500 0.8994397 0.5870588
## 9 0.001 6 15 500 0.8974198 0.5688235
## 16 0.010 6 5 500 0.9296301 0.7852941
## 17 0.010 6 10 500 0.9281216 0.7794118
## 18 0.010 6 15 500 0.9263270 0.7835294
## 25 0.100 6 5 500 0.9599595 0.8970588
## 26 0.100 6 10 500 0.9594183 0.8964706
## 27 0.100 6 15 500 0.9576763 0.8917647
## Spec ROCSD SensSD SpecSD
## 1 0.9513928 0.02487947 0.05615902 0.01890302
## 2 0.9524424 0.02483276 0.05028752 0.01872530
## 3 0.9545294 0.02523211 0.05077968 0.01875308
## 10 0.9087231 0.02224639 0.05438526 0.02364804
## 11 0.9090740 0.02275322 0.05567557 0.02480784
## 12 0.9104683 0.02329341 0.06048511 0.02549016
## 19 0.9359847 0.01682469 0.05293437 0.02263121
## 20 0.9401892 0.01932518 0.05610765 0.02512296
## 21 0.9450801 0.01725417 0.05141454 0.02269976
## 4 0.9478871 0.02361362 0.03962410 0.02093599
## 5 0.9478932 0.02380831 0.03799889 0.01997374
## 6 0.9517346 0.02417257 0.03948742 0.02023755
## 13 0.9094279 0.02116242 0.05599833 0.02578182
## 14 0.9111701 0.02219566 0.05803392 0.02589530
## 15 0.9115271 0.02228931 0.05441176 0.02431051
## 22 0.9408879 0.01822748 0.05435211 0.02470689
## 23 0.9443753 0.01734441 0.04977603 0.02627205
## 24 0.9436949 0.01742780 0.05361096 0.02330818
## 7 0.9485828 0.02293519 0.03921262 0.01949393
## 8 0.9499832 0.02371313 0.03842337 0.02284164
## 9 0.9496384 0.02343156 0.04043449 0.02088433
## 16 0.9132662 0.02095326 0.05147059 0.02429162
## 17 0.9146606 0.02157192 0.05164536 0.02525835
## 18 0.9164119 0.02092553 0.05494571 0.02154276
## 25 0.9405370 0.01648611 0.05164536 0.02569397
## 26 0.9443905 0.01700477 0.05310432 0.02450729
## 27 0.9471762 0.01647188 0.05248991 0.02258879(MBS_GBM_Train_AUROC <- MBS_GBM_Tune$results[MBS_GBM_Tune$results$n.trees==MBS_GBM_Tune$bestTune$n.trees &
MBS_GBM_Tune$results$shrinkage==MBS_GBM_Tune$bestTune$shrinkage &
MBS_GBM_Tune$results$n.minobsinnode==MBS_GBM_Tune$bestTune$n.minobsinnode &
MBS_GBM_Tune$results$interaction.depth==MBS_GBM_Tune$bestTune$interaction.depth,
c("ROC")])## [1] 0.9599595##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_GBM_VarImp <- varImp(MBS_GBM_Tune, scale = TRUE)
plot(MBS_GBM_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariGBMle Importance : Stochastic Gradient Boosting",
xlGBM="Scaled Variable Importance Metrics",
ylGBM="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_GBM_Test <- data.frame(MBS_GBM_Test_Observed = MA_Test$diagnosis,
MBS_GBM_Test_Predicted = predict(MBS_GBM_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_GBM_Test_ROC <- roc(response = MBS_GBM_Test$MBS_GBM_Test_Observed,
predictor = MBS_GBM_Test$MBS_GBM_Test_Predicted.M,
levels = rev(levels(MBS_GBM_Test$MBS_GBM_Test_Observed)))
(MBS_GBM_Test_AUROC <- auc(MBS_GBM_Test_ROC)[1])## [1] 0.9825621.5.3 Extreme Gradient Boosting (MBS_XGB)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
XGB_Grid = expand.grid(nrounds = 500,
max_depth = c(4,5,6),
eta = c(0.2,0.3,0.4),
gamma = c(0.1,0.01,0.001),
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1)
##################################
# Running the extreme gradient boosting model
# by setting the caret method to 'xgbTree'
##################################
set.seed(12345678)
MBS_XGB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "xgbTree",
tuneGrid = XGB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_XGB_Tune## eXtreme Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## eta max_depth gamma ROC Sens Spec
## 0.2 4 0.001 0.9577896 0.8935294 0.9426392
## 0.2 4 0.010 0.9571278 0.8941176 0.9422883
## 0.2 4 0.100 0.9565532 0.8947059 0.9433349
## 0.2 5 0.001 0.9572875 0.8964706 0.9426270
## 0.2 5 0.010 0.9574715 0.8982353 0.9384409
## 0.2 5 0.100 0.9561604 0.8964706 0.9419344
## 0.2 6 0.001 0.9579412 0.8982353 0.9419375
## 0.2 6 0.010 0.9569283 0.8958824 0.9412510
## 0.2 6 0.100 0.9571069 0.8982353 0.9401861
## 0.3 4 0.001 0.9586614 0.8994118 0.9433471
## 0.3 4 0.010 0.9577398 0.8982353 0.9419405
## 0.3 4 0.100 0.9562470 0.8994118 0.9412387
## 0.3 5 0.001 0.9589816 0.8970588 0.9405492
## 0.3 5 0.010 0.9579795 0.8958824 0.9394905
## 0.3 5 0.100 0.9561599 0.8958824 0.9433349
## 0.3 6 0.001 0.9570533 0.8970588 0.9422914
## 0.3 6 0.010 0.9557215 0.8964706 0.9415866
## 0.3 6 0.100 0.9563702 0.8988235 0.9415866
## 0.4 4 0.001 0.9580452 0.8941176 0.9450831
## 0.4 4 0.010 0.9578986 0.8935294 0.9433471
## 0.4 4 0.100 0.9559785 0.8935294 0.9426331
## 0.4 5 0.001 0.9571105 0.8917647 0.9366987
## 0.4 5 0.010 0.9571568 0.8911765 0.9384378
## 0.4 5 0.100 0.9548828 0.8958824 0.9398474
## 0.4 6 0.001 0.9578601 0.8976471 0.9377422
## 0.4 6 0.010 0.9570393 0.9000000 0.9380870
## 0.4 6 0.100 0.9544391 0.8964706 0.9394874
##
## Tuning parameter 'nrounds' was held constant at a value of 500
## Tuning
##
## Tuning parameter 'min_child_weight' was held constant at a value of 1
##
## Tuning parameter 'subsample' was held constant at a value of 1
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were nrounds = 500, max_depth = 5, eta
## = 0.3, gamma = 0.001, colsample_bytree = 1, min_child_weight = 1 and
## subsample = 1.MBS_XGB_Tune$finalModel## ##### xgb.Booster
## raw: 531.8 Kb
## call:
## xgboost::xgb.train(params = list(eta = param$eta, max_depth = param$max_depth,
## gamma = param$gamma, colsample_bytree = param$colsample_bytree,
## min_child_weight = param$min_child_weight, subsample = param$subsample),
## data = x, nrounds = param$nrounds, objective = "binary:logistic")
## params (as set within xgb.train):
## eta = "0.3", max_depth = "5", gamma = "0.001", colsample_bytree = "1", min_child_weight = "1", subsample = "1", objective = "binary:logistic", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.print.evaluation(period = print_every_n)
## # of features: 6
## niter: 500
## nfeatures : 6
## xNames : texture_mean smoothness_mean compactness_se texture_worst smoothness_worst symmetry_worst
## problemType : Classification
## tuneValue :
## nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
## 13 500 5 0.3 0.001 1 1 1
## obsLevels : M B
## param :
## list()MBS_XGB_Tune$results## eta max_depth gamma colsample_bytree min_child_weight subsample nrounds
## 1 0.2 4 0.001 1 1 1 500
## 2 0.2 4 0.010 1 1 1 500
## 3 0.2 4 0.100 1 1 1 500
## 10 0.3 4 0.001 1 1 1 500
## 11 0.3 4 0.010 1 1 1 500
## 12 0.3 4 0.100 1 1 1 500
## 19 0.4 4 0.001 1 1 1 500
## 20 0.4 4 0.010 1 1 1 500
## 21 0.4 4 0.100 1 1 1 500
## 4 0.2 5 0.001 1 1 1 500
## 5 0.2 5 0.010 1 1 1 500
## 6 0.2 5 0.100 1 1 1 500
## 13 0.3 5 0.001 1 1 1 500
## 14 0.3 5 0.010 1 1 1 500
## 15 0.3 5 0.100 1 1 1 500
## 22 0.4 5 0.001 1 1 1 500
## 23 0.4 5 0.010 1 1 1 500
## 24 0.4 5 0.100 1 1 1 500
## 7 0.2 6 0.001 1 1 1 500
## 8 0.2 6 0.010 1 1 1 500
## 9 0.2 6 0.100 1 1 1 500
## 16 0.3 6 0.001 1 1 1 500
## 17 0.3 6 0.010 1 1 1 500
## 18 0.3 6 0.100 1 1 1 500
## 25 0.4 6 0.001 1 1 1 500
## 26 0.4 6 0.010 1 1 1 500
## 27 0.4 6 0.100 1 1 1 500
## ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.9577896 0.8935294 0.9426392 0.01687288 0.05221451 0.02278518
## 2 0.9571278 0.8941176 0.9422883 0.01680119 0.05199315 0.02465544
## 3 0.9565532 0.8947059 0.9433349 0.01807507 0.05313825 0.02178664
## 10 0.9586614 0.8994118 0.9433471 0.01770361 0.04644204 0.02428003
## 11 0.9577398 0.8982353 0.9419405 0.01744703 0.05232484 0.02477863
## 12 0.9562470 0.8994118 0.9412387 0.01711876 0.05279801 0.02353039
## 19 0.9580452 0.8941176 0.9450831 0.01815255 0.05233862 0.02081910
## 20 0.9578986 0.8935294 0.9433471 0.01856427 0.04789394 0.02100887
## 21 0.9559785 0.8935294 0.9426331 0.01912049 0.05490634 0.02360832
## 4 0.9572875 0.8964706 0.9426270 0.01809942 0.05327374 0.02092341
## 5 0.9574715 0.8982353 0.9384409 0.01855274 0.05317893 0.01921827
## 6 0.9561604 0.8964706 0.9419344 0.01741132 0.05542902 0.01875888
## 13 0.9589816 0.8970588 0.9405492 0.01887034 0.04987007 0.01807181
## 14 0.9579795 0.8958824 0.9394905 0.01858169 0.05249677 0.02024547
## 15 0.9561599 0.8958824 0.9433349 0.01773607 0.05351675 0.01793777
## 22 0.9571105 0.8917647 0.9366987 0.01917362 0.05231795 0.02404140
## 23 0.9571568 0.8911765 0.9384378 0.01975233 0.05302281 0.02355760
## 24 0.9548828 0.8958824 0.9398474 0.01984338 0.05232484 0.02505943
## 7 0.9579412 0.8982353 0.9419375 0.01852167 0.04706648 0.02263389
## 8 0.9569283 0.8958824 0.9412510 0.01840797 0.04931041 0.02182010
## 9 0.9571069 0.8982353 0.9401861 0.01786090 0.05662560 0.01908666
## 16 0.9570533 0.8970588 0.9422914 0.01803019 0.05129522 0.02147733
## 17 0.9557215 0.8964706 0.9415866 0.01881557 0.04959467 0.02071598
## 18 0.9563702 0.8988235 0.9415866 0.01801038 0.05425255 0.02129219
## 25 0.9578601 0.8976471 0.9377422 0.01829579 0.05377878 0.02572752
## 26 0.9570393 0.9000000 0.9380870 0.01744502 0.05216617 0.02223410
## 27 0.9544391 0.8964706 0.9394874 0.01859835 0.05444488 0.02178696(MBS_XGB_Train_AUROC <- MBS_XGB_Tune$results[MBS_XGB_Tune$results$nrounds==MBS_XGB_Tune$bestTune$nrounds &
MBS_XGB_Tune$results$max_depth==MBS_XGB_Tune$bestTune$max_depth &
MBS_XGB_Tune$results$eta==MBS_XGB_Tune$bestTune$eta &
MBS_XGB_Tune$results$gamma==MBS_XGB_Tune$bestTune$gamma &
MBS_XGB_Tune$results$colsample_bytree==MBS_XGB_Tune$bestTune$colsample_bytree &
MBS_XGB_Tune$results$min_child_weight==MBS_XGB_Tune$bestTune$min_child_weight &
MBS_XGB_Tune$results$subsample==MBS_XGB_Tune$bestTune$subsample,
c("ROC")])## [1] 0.9589816##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_XGB_VarImp <- varImp(MBS_XGB_Tune, scale = TRUE)
plot(MBS_XGB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariXGBle Importance : Extreme Gradient Boosting",
xlXGB="Scaled Variable Importance Metrics",
ylXGB="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBS_XGB_Test <- data.frame(MBS_XGB_Test_Observed = MA_Test$diagnosis,
MBS_XGB_Test_Predicted = predict(MBS_XGB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_XGB_Test_ROC <- roc(response = MBS_XGB_Test$MBS_XGB_Test_Observed,
predictor = MBS_XGB_Test$MBS_XGB_Test_Predicted.M,
levels = rev(levels(MBS_XGB_Test$MBS_XGB_Test_Observed)))
(MBS_XGB_Test_AUROC <- auc(MBS_XGB_Test_ROC)[1])## [1] 0.98306511.6 Model Bagging
1.6.1 Random Forest (MBG_RF)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
RF_Grid = data.frame(mtry = c(25,75,125))
##################################
# Running the random forest model
# by setting the caret method to 'rf'
##################################
set.seed(12345678)
MBG_RF_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rf",
tuneGrid = RF_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_RF_Tune## Random Forest
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 25 0.9605251 0.8929412 0.9416018
## 75 0.9609714 0.8970588 0.9436949
## 125 0.9602112 0.8964706 0.9423005
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 75.MBG_RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 6
##
## OOB estimate of error rate: 4.17%
## Confusion matrix:
## M B class.error
## M 322 18 0.05294118
## B 20 552 0.03496503MBG_RF_Tune$results## mtry ROC Sens Spec ROCSD SensSD SpecSD
## 1 25 0.9605251 0.8929412 0.9416018 0.01542129 0.05068021 0.02199494
## 2 75 0.9609714 0.8970588 0.9436949 0.01509025 0.05129522 0.02118903
## 3 125 0.9602112 0.8964706 0.9423005 0.01661490 0.05067309 0.02317906(MBG_RF_Train_AUROC <- MBG_RF_Tune$results[MBG_RF_Tune$results$mtry==MBG_RF_Tune$bestTune$mtry,
c("ROC")])## [1] 0.9609714##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_RF_VarImp <- varImp(MBG_RF_Tune, scale = TRUE)
plot(MBG_RF_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Random Forest",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBG_RF_Test <- data.frame(MBG_RF_Test_Observed = MA_Test$diagnosis,
MBG_RF_Test_Predicted = predict(MBG_RF_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_RF_Test_ROC <- roc(response = MBG_RF_Test$MBG_RF_Test_Observed,
predictor = MBG_RF_Test$MBG_RF_Test_Predicted.M,
levels = rev(levels(MBG_RF_Test$MBG_RF_Test_Observed)))
(MBG_RF_Test_AUROC <- auc(MBG_RF_Test_ROC)[1])## [1] 0.99354461.6.2 Bagged Classification and Regression Trees (MBG_BTREE)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the bagged CART model
# by setting the caret method to 'treebag'
##################################
set.seed(12345678)
MBG_BTREE_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "treebag",
nbagg = 50,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_BTREE_Tune## Bagged CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.957904 0.8976471 0.9457818MBG_BTREE_Tune$finalModel##
## Bagging classification trees with 50 bootstrap replicationsMBG_BTREE_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.957904 0.8976471 0.9457818 0.01659441 0.04793155 0.02103826(MBG_BTREE_Train_AUROC <- MBG_BTREE_Tune$results$ROC)## [1] 0.957904##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_BTREE_VarImp <- varImp(MBG_BTREE_Tune, scale = TRUE)
plot(MBG_BTREE_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Bagged Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
MBG_BTREE_Test <- data.frame(MBG_BTREE_Test_Observed = MA_Test$diagnosis,
MBG_BTREE_Test_Predicted = predict(MBG_BTREE_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_BTREE_Test_ROC <- roc(response = MBG_BTREE_Test$MBG_BTREE_Test_Observed,
predictor = MBG_BTREE_Test$MBG_BTREE_Test_Predicted.M,
levels = rev(levels(MBG_BTREE_Test$MBG_BTREE_Test_Observed)))
(MBG_BTREE_Test_AUROC <- auc(MBG_BTREE_Test_ROC)[1])## [1] 0.99287391.7 Model Ensemble and Stacking
1.7.1 Base Learner Model Development using Linear Discriminant Analysis (BAL_LDA)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the linear discriminant analysis model
# by setting the caret method to 'lda'
##################################
set.seed(12345678)
BAL_LDA_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "lda",
preProc = c("center","scale"),
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_LDA_Tune## Linear Discriminant Analysis
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8736974 0.6988235 0.8831976BAL_LDA_Tune$finalModel## Call:
## lda(x, y)
##
## Prior probabilities of groups:
## M B
## 0.372807 0.627193
##
## Group means:
## texture_mean smoothness_mean compactness_se texture_worst smoothness_worst
## M 0.5472116 0.4765617 0.4590688 0.5971745 0.5506486
## B -0.3252656 -0.2832710 -0.2728731 -0.3549639 -0.3273086
## symmetry_worst
## M 0.4949332
## B -0.2941911
##
## Coefficients of linear discriminants:
## LD1
## texture_mean -0.5101493
## smoothness_mean -0.2598153
## compactness_se -0.2404049
## texture_worst -0.2745054
## smoothness_worst -0.3118616
## symmetry_worst -0.3480006BAL_LDA_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.8736974 0.6988235 0.8831976 0.02765351 0.05043067 0.0340081(BAL_LDA_Train_AUROC <- BAL_LDA_Tune$results$ROC)## [1] 0.8736974##################################
# Identifying and plotting the
# best model predictors
##################################
BAL_LDA_VarImp <- varImp(BAL_LDA_Tune, scale = TRUE)
plot(BAL_LDA_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Linear Discriminant Analysis",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_LDA_Test <- data.frame(BAL_LDA_Test_Observed = MA_Test$diagnosis,
BAL_LDA_Test_Predicted = predict(BAL_LDA_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_LDA_Test## BAL_LDA_Test_Observed BAL_LDA_Test_Predicted.M BAL_LDA_Test_Predicted.B
## 8 M 0.847694277 0.15230572
## 13 M 0.752143565 0.24785644
## 16 M 0.984315261 0.01568474
## 19 M 0.648181426 0.35181857
## 24 M 0.622861398 0.37713860
## 31 M 0.917386313 0.08261369
## 37 M 0.793173364 0.20682664
## 42 M 0.907919179 0.09208082
## 44 M 0.764230707 0.23576929
## 46 M 0.728618101 0.27138190
## 48 M 0.859933443 0.14006656
## 54 M 0.491166745 0.50883326
## 61 B 0.156553430 0.84344657
## 65 M 0.944349947 0.05565005
## 69 B 0.781343429 0.21865657
## 70 B 0.048409216 0.95159078
## 71 M 0.248016285 0.75198371
## 72 B 0.071364929 0.92863507
## 82 B 0.539918492 0.46008151
## 89 B 0.527909798 0.47209020
## 93 B 0.007363099 0.99263690
## 98 B 0.155923262 0.84407674
## 103 B 0.143938966 0.85606103
## 104 B 0.481557196 0.51844280
## 107 B 0.675382693 0.32461731
## 109 M 0.958051423 0.04194858
## 116 B 0.481356202 0.51864380
## 123 M 0.935249986 0.06475001
## 125 B 0.021730402 0.97826960
## 129 B 0.262831191 0.73716881
## 130 M 0.836436129 0.16356387
## 136 M 0.544334142 0.45566586
## 139 M 0.567922485 0.43207751
## 147 M 0.805510959 0.19448904
## 149 B 0.119929629 0.88007037
## 150 B 0.035624393 0.96437561
## 162 M 0.048283884 0.95171612
## 167 B 0.003383242 0.99661676
## 185 M 0.494169519 0.50583048
## 191 M 0.984766695 0.01523330
## 194 M 0.965697843 0.03430216
## 197 M 0.923211780 0.07678822
## 200 M 0.829831914 0.17016809
## 201 B 0.397595099 0.60240490
## 209 B 0.832870585 0.16712942
## 211 M 0.490217911 0.50978209
## 213 M 0.051277757 0.94872224
## 217 B 0.578278588 0.42172141
## 221 B 0.028330155 0.97166984
## 222 B 0.127351126 0.87264887
## 223 B 0.321834029 0.67816597
## 228 B 0.080646732 0.91935327
## 237 M 0.878798075 0.12120193
## 239 B 0.386770703 0.61322930
## 242 B 0.017488284 0.98251172
## 246 B 0.572817303 0.42718270
## 256 M 0.450075130 0.54992487
## 259 M 0.910731982 0.08926802
## 262 M 0.300394459 0.69960554
## 263 M 0.556262598 0.44373740
## 266 M 0.914981868 0.08501813
## 272 B 0.037223108 0.96277689
## 274 B 0.115142932 0.88485707
## 275 M 0.477067718 0.52293228
## 285 B 0.017755227 0.98224477
## 300 B 0.221001328 0.77899867
## 308 B 0.009577361 0.99042264
## 328 B 0.017205318 0.98279468
## 345 B 0.152736274 0.84726373
## 349 B 0.125805661 0.87419434
## 356 B 0.100082662 0.89991734
## 363 B 0.279268326 0.72073167
## 365 B 0.067382598 0.93261740
## 368 B 0.289130560 0.71086944
## 382 B 0.059008473 0.94099153
## 383 B 0.099940629 0.90005937
## 387 B 0.029501526 0.97049847
## 388 B 0.011594911 0.98840509
## 401 M 0.916015243 0.08398476
## 403 B 0.121740628 0.87825937
## 417 B 0.688697152 0.31130285
## 420 B 0.438908267 0.56109173
## 428 B 0.538028898 0.46197110
## 434 M 0.754075514 0.24592449
## 442 M 0.720665044 0.27933496
## 444 B 0.060993346 0.93900665
## 445 M 0.127952064 0.87204794
## 454 B 0.063780108 0.93621989
## 455 B 0.116970237 0.88302976
## 460 B 0.398746415 0.60125359
## 462 M 0.848632474 0.15136753
## 463 B 0.214670122 0.78532988
## 472 B 0.424438993 0.57556101
## 484 B 0.099533312 0.90046669
## 489 B 0.266301333 0.73369867
## 493 M 0.569012819 0.43098718
## 494 B 0.003052093 0.99694791
## 497 B 0.533620893 0.46637911
## 498 B 0.182516881 0.81748312
## 501 B 0.056152054 0.94384795
## 502 M 0.965124814 0.03487519
## 507 B 0.469228787 0.53077121
## 509 B 0.047121869 0.95287813
## 525 B 0.140788342 0.85921166
## 526 B 0.147559515 0.85244048
## 527 B 0.638000064 0.36199994
## 531 B 0.288554915 0.71144509
## 532 B 0.651049032 0.34895097
## 534 M 0.415894843 0.58410516
## 537 M 0.705945640 0.29405436
## 544 B 0.493009474 0.50699053
## 546 B 0.442267183 0.55773282
## 548 B 0.210657721 0.78934228
## 550 B 0.511181378 0.48881862
## 551 B 0.067861362 0.93213864
## 556 B 0.672400732 0.32759927
## 557 B 0.194179282 0.80582072
## 575 M 0.804848412 0.19515159
## 578 M 0.961876926 0.03812307
## 581 M 0.641329989 0.35867001
## 583 M 0.428761786 0.57123821
## 589 B 0.116915613 0.88308439
## 590 B 0.225642380 0.77435762
## 601 M 0.892598780 0.10740122
## 603 M 0.904195640 0.09580436
## 611 M 0.907919179 0.09208082
## 617 M 0.859933443 0.14006656
## 619 B 0.375773201 0.62422680
## 625 B 0.221742438 0.77825756
## 628 B 0.043025434 0.95697457
## 632 M 0.860984905 0.13901509
## 646 B 0.055755243 0.94424476
## 649 B 0.248132208 0.75186779
## 657 M 0.747532205 0.25246780
## 662 B 0.007363099 0.99263690
## 665 M 0.729748372 0.27025163
## 677 B 0.169948174 0.83005183
## 679 B 0.579052997 0.42094700
## 685 B 0.481356202 0.51864380
## 687 M 0.797009318 0.20299068
## 689 M 0.379875103 0.62012490
## 695 B 0.052838610 0.94716139
## 701 M 0.524264466 0.47573553
## 704 M 0.632072177 0.36792782
## 706 B 0.051096277 0.94890372
## 709 B 0.055780027 0.94421997
## 715 B 0.209980973 0.79001903
## 726 M 0.588590272 0.41140973
## 734 M 0.556955275 0.44304472
## 747 M 0.650003433 0.34999657
## 752 M 0.696270542 0.30372946
## 763 M 0.965697843 0.03430216
## 765 B 0.068697986 0.93130201
## 775 M 0.266641278 0.73335872
## 780 M 0.490217911 0.50978209
## 786 B 0.578278588 0.42172141
## 792 B 0.321834029 0.67816597
## 796 B 0.081823993 0.91817601
## 809 M 0.971682884 0.02831712
## 813 B 0.213421626 0.78657837
## 816 B 0.079525623 0.92047438
## 818 B 0.817347399 0.18265260
## 820 M 0.681962716 0.31803728
## 823 M 0.336004147 0.66399585
## 850 M 0.944642119 0.05535788
## 854 B 0.017755227 0.98224477
## 865 B 0.021146573 0.97885343
## 867 M 0.051514297 0.94848570
## 870 M 0.652912941 0.34708706
## 876 B 0.033179561 0.96682044
## 882 B 0.029474840 0.97052516
## 886 B 0.004334774 0.99566523
## 895 B 0.157840339 0.84215966
## 896 B 0.013873786 0.98612621
## 905 M 0.647891355 0.35210865
## 906 B 0.032661361 0.96733864
## 913 M 0.809506116 0.19049388
## 917 B 0.063686686 0.93631331
## 919 B 0.196123444 0.80387656
## 922 M 0.720066525 0.27993347
## 923 M 0.853122900 0.14687710
## 925 B 0.100082662 0.89991734
## 928 B 0.027529187 0.97247081
## 932 B 0.279268326 0.72073167
## 936 M 0.883947846 0.11605215
## 941 B 0.010656141 0.98934386
## 950 B 0.303759143 0.69624086
## 953 B 0.403505824 0.59649418
## 956 B 0.029501526 0.97049847
## 967 B 0.026289767 0.97371023
## 973 B 0.164651800 0.83534820
## 974 B 0.011316735 0.98868327
## 976 B 0.059212838 0.94078716
## 980 B 0.367183351 0.63281665
## 985 B 0.640716764 0.35928324
## 987 M 0.778924037 0.22107596
## 993 B 0.238399926 0.76160007
## 1010 B 0.293568883 0.70643112
## 1015 B 0.640576383 0.35942362
## 1023 B 0.063780108 0.93621989
## 1025 B 0.634929643 0.36507036
## 1030 M 0.893535322 0.10646468
## 1034 B 0.062032839 0.93796716
## 1043 B 0.306855621 0.69314438
## 1045 B 0.119438841 0.88056116
## 1046 B 0.281449575 0.71855042
## 1047 B 0.020135949 0.97986405
## 1052 B 0.140996234 0.85900377
## 1060 B 0.423288399 0.57671160
## 1061 B 0.002198833 0.99780117
## 1066 B 0.533620893 0.46637911
## 1068 M 0.279349586 0.72065041
## 1071 M 0.965124814 0.03487519
## 1072 B 0.409662210 0.59033779
## 1077 B 0.473035808 0.52696419
## 1090 B 0.596599276 0.40340072
## 1094 B 0.140788342 0.85921166
## 1097 B 0.034350449 0.96564955
## 1098 B 0.067500501 0.93249950
## 1105 M 0.539179210 0.46082079
## 1106 M 0.705945640 0.29405436
## 1109 B 0.851517966 0.14848203
## 1113 B 0.493009474 0.50699053
## 1129 B 0.516810172 0.48318983
## 1136 M 0.536782216 0.46321778
## 1138 B 0.044202476 0.95579752##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_LDA_Test_ROC <- roc(response = BAL_LDA_Test$BAL_LDA_Test_Observed,
predictor = BAL_LDA_Test$BAL_LDA_Test_Predicted.M,
levels = rev(levels(BAL_LDA_Test$BAL_LDA_Test_Observed)))
(BAL_LDA_Test_AUROC <- auc(BAL_LDA_Test_ROC)[1])## [1] 0.89847421.7.2 Base Learner Model Development using Classification and Regression Trees (BAL_CART)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
CART_Grid = data.frame(cp = c(0.001, 0.005, 0.010, 0.015, 0.020))
##################################
# Running the classification and regression tree model
# by setting the caret method to 'rpart'
##################################
set.seed(12345678)
BAL_CART_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rpart",
tuneGrid = CART_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_CART_Tune## CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## cp ROC Sens Spec
## 0.001 0.8699967 0.7600000 0.8688787
## 0.005 0.8552277 0.7570588 0.8692357
## 0.010 0.8414964 0.7535294 0.8699252
## 0.015 0.8326147 0.7429412 0.8734249
## 0.020 0.8298568 0.7323529 0.8744744
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.001.BAL_CART_Tune$finalModel## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 340 B (0.37280702 0.62719298)
## 2) texture_mean>=2.927988 473 196 M (0.58562368 0.41437632)
## 4) smoothness_worst>=-1.52382 252 45 M (0.82142857 0.17857143)
## 8) symmetry_worst>=-1.660064 150 15 M (0.90000000 0.10000000)
## 16) smoothness_worst>=-1.471923 114 5 M (0.95614035 0.04385965) *
## 17) smoothness_worst< -1.471923 36 10 M (0.72222222 0.27777778)
## 34) texture_mean>=3.007903 27 4 M (0.85185185 0.14814815) *
## 35) texture_mean< 3.007903 9 3 B (0.33333333 0.66666667) *
## 9) symmetry_worst< -1.660064 102 30 M (0.70588235 0.29411765)
## 18) texture_mean>=3.079152 51 9 M (0.82352941 0.17647059) *
## 19) texture_mean< 3.079152 51 21 M (0.58823529 0.41176471)
## 38) compactness_se>=-3.816486 28 6 M (0.78571429 0.21428571)
## 76) texture_mean< 3.011847 17 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=3.011847 11 5 B (0.45454545 0.54545455) *
## 39) compactness_se< -3.816486 23 8 B (0.34782609 0.65217391)
## 78) smoothness_mean< -2.312955 10 4 M (0.60000000 0.40000000) *
## 79) smoothness_mean>=-2.312955 13 2 B (0.15384615 0.84615385) *
## 5) smoothness_worst< -1.52382 221 70 B (0.31674208 0.68325792)
## 10) smoothness_mean>=-2.425944 88 43 M (0.51136364 0.48863636)
## 20) texture_worst>=4.527762 58 18 M (0.68965517 0.31034483)
## 40) symmetry_worst>=-1.966444 35 5 M (0.85714286 0.14285714) *
## 41) symmetry_worst< -1.966444 23 10 B (0.43478261 0.56521739)
## 82) texture_worst< 4.804356 13 5 M (0.61538462 0.38461538) *
## 83) texture_worst>=4.804356 10 2 B (0.20000000 0.80000000) *
## 21) texture_worst< 4.527762 30 5 B (0.16666667 0.83333333) *
## 11) smoothness_mean< -2.425944 133 25 B (0.18796992 0.81203008)
## 22) symmetry_worst>=-1.496954 8 2 M (0.75000000 0.25000000) *
## 23) symmetry_worst< -1.496954 125 19 B (0.15200000 0.84800000)
## 46) symmetry_worst>=-1.695215 24 8 B (0.33333333 0.66666667)
## 92) compactness_se< -4.114414 13 5 M (0.61538462 0.38461538) *
## 93) compactness_se>=-4.114414 11 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.695215 101 11 B (0.10891089 0.89108911)
## 94) compactness_se>=-3.611049 28 8 B (0.28571429 0.71428571)
## 188) compactness_se< -3.197021 9 3 M (0.66666667 0.33333333) *
## 189) compactness_se>=-3.197021 19 2 B (0.10526316 0.89473684) *
## 95) compactness_se< -3.611049 73 3 B (0.04109589 0.95890411) *
## 3) texture_mean< 2.927988 439 63 B (0.14350797 0.85649203)
## 6) symmetry_worst>=-1.325507 25 4 M (0.84000000 0.16000000) *
## 7) symmetry_worst< -1.325507 414 42 B (0.10144928 0.89855072)
## 14) compactness_se>=-3.970723 153 33 B (0.21568627 0.78431373)
## 28) smoothness_worst>=-1.451541 30 13 M (0.56666667 0.43333333)
## 56) symmetry_worst>=-1.619683 14 1 M (0.92857143 0.07142857) *
## 57) symmetry_worst< -1.619683 16 4 B (0.25000000 0.75000000) *
## 29) smoothness_worst< -1.451541 123 16 B (0.13008130 0.86991870)
## 58) compactness_se< -3.427747 70 15 B (0.21428571 0.78571429)
## 116) compactness_se>=-3.705619 28 11 B (0.39285714 0.60714286)
## 232) smoothness_mean>=-2.388266 17 7 M (0.58823529 0.41176471) *
## 233) smoothness_mean< -2.388266 11 1 B (0.09090909 0.90909091) *
## 117) compactness_se< -3.705619 42 4 B (0.09523810 0.90476190) *
## 59) compactness_se>=-3.427747 53 1 B (0.01886792 0.98113208) *
## 15) compactness_se< -3.970723 261 9 B (0.03448276 0.96551724) *BAL_CART_Tune$results## cp ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.001 0.8699967 0.7600000 0.8688787 0.02958365 0.05611407 0.02331264
## 2 0.005 0.8552277 0.7570588 0.8692357 0.02789001 0.05975368 0.02778803
## 3 0.010 0.8414964 0.7535294 0.8699252 0.02430214 0.05134438 0.02773026
## 4 0.015 0.8326147 0.7429412 0.8734249 0.02618044 0.05304320 0.01922145
## 5 0.020 0.8298568 0.7323529 0.8744744 0.02663895 0.06018642 0.02293389(BAL_CART_Train_AUROC <- BAL_CART_Tune$results[BAL_CART_Tune$results$cp==BAL_CART_Tune$bestTune$cp,
c("ROC")])## [1] 0.8699967##################################
# Identifying and plotting the
# best model predictors
##################################
BAL_CART_VarImp <- varImp(BAL_CART_Tune, scale = TRUE)
plot(BAL_CART_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_CART_Test <- data.frame(BAL_CART_Test_Observed = MA_Test$diagnosis,
BAL_CART_Test_Predicted = predict(BAL_CART_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_CART_Test## BAL_CART_Test_Observed BAL_CART_Test_Predicted.M BAL_CART_Test_Predicted.B
## 8 M 0.95614035 0.04385965
## 13 M 0.85714286 0.14285714
## 16 M 0.95614035 0.04385965
## 19 M 0.82352941 0.17647059
## 24 M 0.82352941 0.17647059
## 31 M 0.95614035 0.04385965
## 37 M 0.95614035 0.04385965
## 42 M 0.15384615 0.84615385
## 44 M 0.95614035 0.04385965
## 46 M 0.84000000 0.16000000
## 48 M 0.84000000 0.16000000
## 54 M 0.16666667 0.83333333
## 61 B 0.03448276 0.96551724
## 65 M 0.95614035 0.04385965
## 69 B 0.84000000 0.16000000
## 70 B 0.03448276 0.96551724
## 71 M 0.85714286 0.14285714
## 72 B 0.01886792 0.98113208
## 82 B 0.92857143 0.07142857
## 89 B 0.00000000 1.00000000
## 93 B 0.03448276 0.96551724
## 98 B 0.15384615 0.84615385
## 103 B 0.04109589 0.95890411
## 104 B 0.15384615 0.84615385
## 107 B 0.25000000 0.75000000
## 109 M 0.95614035 0.04385965
## 116 B 0.45454545 0.54545455
## 123 M 0.95614035 0.04385965
## 125 B 0.01886792 0.98113208
## 129 B 0.01886792 0.98113208
## 130 M 0.85714286 0.14285714
## 136 M 0.82352941 0.17647059
## 139 M 0.01886792 0.98113208
## 147 M 0.84000000 0.16000000
## 149 B 0.09523810 0.90476190
## 150 B 0.03448276 0.96551724
## 162 M 0.09090909 0.90909091
## 167 B 0.03448276 0.96551724
## 185 M 0.85714286 0.14285714
## 191 M 0.95614035 0.04385965
## 194 M 0.95614035 0.04385965
## 197 M 0.95614035 0.04385965
## 200 M 0.95614035 0.04385965
## 201 B 0.60000000 0.40000000
## 209 B 0.85185185 0.14814815
## 211 M 0.85714286 0.14285714
## 213 M 0.58823529 0.41176471
## 217 B 0.01886792 0.98113208
## 221 B 0.03448276 0.96551724
## 222 B 0.09523810 0.90476190
## 223 B 0.03448276 0.96551724
## 228 B 0.09090909 0.90909091
## 237 M 0.95614035 0.04385965
## 239 B 0.66666667 0.33333333
## 242 B 0.03448276 0.96551724
## 246 B 0.15384615 0.84615385
## 256 M 0.92857143 0.07142857
## 259 M 0.95614035 0.04385965
## 262 M 0.04109589 0.95890411
## 263 M 0.85714286 0.14285714
## 266 M 0.82352941 0.17647059
## 272 B 0.03448276 0.96551724
## 274 B 0.03448276 0.96551724
## 275 M 0.82352941 0.17647059
## 285 B 0.01886792 0.98113208
## 300 B 0.16666667 0.83333333
## 308 B 0.03448276 0.96551724
## 328 B 0.03448276 0.96551724
## 345 B 0.03448276 0.96551724
## 349 B 0.03448276 0.96551724
## 356 B 0.10526316 0.89473684
## 363 B 0.85714286 0.14285714
## 365 B 0.03448276 0.96551724
## 368 B 0.03448276 0.96551724
## 382 B 0.09523810 0.90476190
## 383 B 0.10526316 0.89473684
## 387 B 0.09090909 0.90909091
## 388 B 0.03448276 0.96551724
## 401 M 0.95614035 0.04385965
## 403 B 0.01886792 0.98113208
## 417 B 0.15384615 0.84615385
## 420 B 0.85714286 0.14285714
## 428 B 0.00000000 1.00000000
## 434 M 0.85185185 0.14814815
## 442 M 0.82352941 0.17647059
## 444 B 0.03448276 0.96551724
## 445 M 0.03448276 0.96551724
## 454 B 0.03448276 0.96551724
## 455 B 0.03448276 0.96551724
## 460 B 0.04109589 0.95890411
## 462 M 0.82352941 0.17647059
## 463 B 0.04109589 0.95890411
## 472 B 0.04109589 0.95890411
## 484 B 0.03448276 0.96551724
## 489 B 0.03448276 0.96551724
## 493 M 0.85185185 0.14814815
## 494 B 0.03448276 0.96551724
## 497 B 0.92857143 0.07142857
## 498 B 0.03448276 0.96551724
## 501 B 0.03448276 0.96551724
## 502 M 0.95614035 0.04385965
## 507 B 0.15384615 0.84615385
## 509 B 0.03448276 0.96551724
## 525 B 0.09523810 0.90476190
## 526 B 0.03448276 0.96551724
## 527 B 0.95614035 0.04385965
## 531 B 0.03448276 0.96551724
## 532 B 0.95614035 0.04385965
## 534 M 0.85714286 0.14285714
## 537 M 0.82352941 0.17647059
## 544 B 0.04109589 0.95890411
## 546 B 0.85714286 0.14285714
## 548 B 0.09090909 0.90909091
## 550 B 0.00000000 1.00000000
## 551 B 0.04109589 0.95890411
## 556 B 0.82352941 0.17647059
## 557 B 0.16666667 0.83333333
## 575 M 0.84000000 0.16000000
## 578 M 0.95614035 0.04385965
## 581 M 0.84000000 0.16000000
## 583 M 0.66666667 0.33333333
## 589 B 0.03448276 0.96551724
## 590 B 0.09523810 0.90476190
## 601 M 0.95614035 0.04385965
## 603 M 0.95614035 0.04385965
## 611 M 0.15384615 0.84615385
## 617 M 0.84000000 0.16000000
## 619 B 0.04109589 0.95890411
## 625 B 0.16666667 0.83333333
## 628 B 0.04109589 0.95890411
## 632 M 0.82352941 0.17647059
## 646 B 0.58823529 0.41176471
## 649 B 0.03448276 0.96551724
## 657 M 0.85714286 0.14285714
## 662 B 0.03448276 0.96551724
## 665 M 0.85714286 0.14285714
## 677 B 0.03448276 0.96551724
## 679 B 0.60000000 0.40000000
## 685 B 0.45454545 0.54545455
## 687 M 0.92857143 0.07142857
## 689 M 0.75000000 0.25000000
## 695 B 0.03448276 0.96551724
## 701 M 0.15384615 0.84615385
## 704 M 0.95614035 0.04385965
## 706 B 0.03448276 0.96551724
## 709 B 0.01886792 0.98113208
## 715 B 0.01886792 0.98113208
## 726 M 0.45454545 0.54545455
## 734 M 0.75000000 0.25000000
## 747 M 0.33333333 0.66666667
## 752 M 0.85185185 0.14814815
## 763 M 0.95614035 0.04385965
## 765 B 0.03448276 0.96551724
## 775 M 0.92857143 0.07142857
## 780 M 0.85714286 0.14285714
## 786 B 0.01886792 0.98113208
## 792 B 0.03448276 0.96551724
## 796 B 0.03448276 0.96551724
## 809 M 0.82352941 0.17647059
## 813 B 0.04109589 0.95890411
## 816 B 0.03448276 0.96551724
## 818 B 0.95614035 0.04385965
## 820 M 0.85714286 0.14285714
## 823 M 0.03448276 0.96551724
## 850 M 0.95614035 0.04385965
## 854 B 0.01886792 0.98113208
## 865 B 0.03448276 0.96551724
## 867 M 0.03448276 0.96551724
## 870 M 1.00000000 0.00000000
## 876 B 0.03448276 0.96551724
## 882 B 0.09090909 0.90909091
## 886 B 0.03448276 0.96551724
## 895 B 0.03448276 0.96551724
## 896 B 0.03448276 0.96551724
## 905 M 0.15384615 0.84615385
## 906 B 0.09523810 0.90476190
## 913 M 0.85714286 0.14285714
## 917 B 0.03448276 0.96551724
## 919 B 0.09523810 0.90476190
## 922 M 0.84000000 0.16000000
## 923 M 0.82352941 0.17647059
## 925 B 0.10526316 0.89473684
## 928 B 0.09090909 0.90909091
## 932 B 0.85714286 0.14285714
## 936 M 0.85714286 0.14285714
## 941 B 0.03448276 0.96551724
## 950 B 0.03448276 0.96551724
## 953 B 0.01886792 0.98113208
## 956 B 0.09090909 0.90909091
## 967 B 0.01886792 0.98113208
## 973 B 0.03448276 0.96551724
## 974 B 0.03448276 0.96551724
## 976 B 0.03448276 0.96551724
## 980 B 0.03448276 0.96551724
## 985 B 0.85185185 0.14814815
## 987 M 0.95614035 0.04385965
## 993 B 0.85714286 0.14285714
## 1010 B 0.01886792 0.98113208
## 1015 B 0.82352941 0.17647059
## 1023 B 0.03448276 0.96551724
## 1025 B 0.20000000 0.80000000
## 1030 M 0.95614035 0.04385965
## 1034 B 0.03448276 0.96551724
## 1043 B 0.04109589 0.95890411
## 1045 B 0.03448276 0.96551724
## 1046 B 0.61538462 0.38461538
## 1047 B 0.03448276 0.96551724
## 1052 B 0.03448276 0.96551724
## 1060 B 0.61538462 0.38461538
## 1061 B 0.03448276 0.96551724
## 1066 B 0.92857143 0.07142857
## 1068 M 0.58823529 0.41176471
## 1071 M 0.95614035 0.04385965
## 1072 B 0.03448276 0.96551724
## 1077 B 0.25000000 0.75000000
## 1090 B 0.84000000 0.16000000
## 1094 B 0.09523810 0.90476190
## 1097 B 0.03448276 0.96551724
## 1098 B 0.01886792 0.98113208
## 1105 M 0.85714286 0.14285714
## 1106 M 0.82352941 0.17647059
## 1109 B 0.82352941 0.17647059
## 1113 B 0.04109589 0.95890411
## 1129 B 0.20000000 0.80000000
## 1136 M 0.66666667 0.33333333
## 1138 B 0.04109589 0.95890411##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_CART_Test_ROC <- roc(response = BAL_CART_Test$BAL_CART_Test_Observed,
predictor = BAL_CART_Test$BAL_CART_Test_Predicted.M,
levels = rev(levels(BAL_CART_Test$BAL_CART_Test_Observed)))
(BAL_CART_Test_AUROC <- auc(BAL_CART_Test_ROC)[1])## [1] 0.88434781.7.3 Base Learner Model Development using Support Vector Machine - Radial Basis Function Kernel (BAL_SVM_R)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# used a range of default values
##################################
# Running the support vector machine model
# by setting the caret method to 'svmRadial'
##################################
set.seed(12345678)
BAL_SVM_R_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "svmRadial",
preProc = c("center", "scale"),
tuneLength = 14,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_SVM_R_Tune## Support Vector Machines with Radial Basis Function Kernel
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## C ROC Sens Spec
## 0.25 0.8785695 0.7035294 0.9108375
## 0.50 0.8841244 0.7217647 0.9090831
## 1.00 0.8901422 0.7335294 0.9048848
## 2.00 0.8994429 0.7364706 0.9052326
## 4.00 0.9053698 0.7441176 0.9006834
## 8.00 0.9055011 0.7458824 0.8961373
## 16.00 0.9086274 0.7494118 0.9003295
## 32.00 0.9095067 0.7558824 0.9010191
## 64.00 0.9071082 0.7635294 0.9020809
## 128.00 0.9068724 0.7647059 0.9097574
## 256.00 0.9078494 0.7900000 0.9153623
## 512.00 0.9064750 0.8000000 0.9241190
## 1024.00 0.9055970 0.8076471 0.9262029
## 2048.00 0.9030325 0.8164706 0.9286621
##
## Tuning parameter 'sigma' was held constant at a value of 0.2133227
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.2133227 and C = 32.BAL_SVM_R_Tune$finalModel## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 32
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 0.213322651914923
##
## Number of Support Vectors : 356
##
## Objective Function Value : -6239.218
## Training error : 0.08114
## Probability model included.BAL_SVM_R_Tune$results## sigma C ROC Sens Spec ROCSD SensSD
## 1 0.2133227 0.25 0.8785695 0.7035294 0.9108375 0.02404759 0.04721176
## 2 0.2133227 0.50 0.8841244 0.7217647 0.9090831 0.02300940 0.04949281
## 3 0.2133227 1.00 0.8901422 0.7335294 0.9048848 0.02180995 0.04479062
## 4 0.2133227 2.00 0.8994429 0.7364706 0.9052326 0.01913779 0.04531067
## 5 0.2133227 4.00 0.9053698 0.7441176 0.9006834 0.01706242 0.04950738
## 6 0.2133227 8.00 0.9055011 0.7458824 0.8961373 0.01610225 0.05328050
## 7 0.2133227 16.00 0.9086274 0.7494118 0.9003295 0.01630832 0.05259281
## 8 0.2133227 32.00 0.9095067 0.7558824 0.9010191 0.01839949 0.04950738
## 9 0.2133227 64.00 0.9071082 0.7635294 0.9020809 0.01952445 0.05128116
## 10 0.2133227 128.00 0.9068724 0.7647059 0.9097574 0.01936122 0.05469587
## 11 0.2133227 256.00 0.9078494 0.7900000 0.9153623 0.02066747 0.05259966
## 12 0.2133227 512.00 0.9064750 0.8000000 0.9241190 0.02410424 0.04746303
## 13 0.2133227 1024.00 0.9055970 0.8076471 0.9262029 0.02220728 0.05972351
## 14 0.2133227 2048.00 0.9030325 0.8164706 0.9286621 0.02363041 0.05729006
## SpecSD
## 1 0.02975325
## 2 0.03050958
## 3 0.03145146
## 4 0.02573695
## 5 0.02473761
## 6 0.02506890
## 7 0.02219347
## 8 0.02905519
## 9 0.02835256
## 10 0.03106407
## 11 0.02789321
## 12 0.02662871
## 13 0.02696224
## 14 0.02368710(BAL_SVM_R_Train_AUROC <- BAL_SVM_R_Tune$results[BAL_SVM_R_Tune$results$C==BAL_SVM_R_Tune$bestTune$C,
c("ROC")])## [1] 0.9095067##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_SVM_R_Test <- data.frame(BAL_SVM_R_Test_Observed = MA_Test$diagnosis,
BAL_SVM_R_Test_Predicted = predict(BAL_SVM_R_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_SVM_R_Test## BAL_SVM_R_Test_Observed BAL_SVM_R_Test_Predicted.M
## 1 M 0.9044222829
## 2 M 0.7290596096
## 3 M 0.9233468355
## 4 M 0.7404179815
## 5 M 0.7945116889
## 6 M 0.9170819657
## 7 M 0.9156025332
## 8 M 0.1669205011
## 9 M 0.9758341031
## 10 M 0.9647140381
## 11 M 0.9932556308
## 12 M 0.4508001411
## 13 B 0.0003314662
## 14 M 0.9021609771
## 15 B 0.2047716701
## 16 B 0.0707212058
## 17 M 0.3236152237
## 18 B 0.0187507336
## 19 B 0.7446307535
## 20 B 0.5427955060
## 21 B 0.0068373114
## 22 B 0.1947931307
## 23 B 0.5253099013
## 24 B 0.5044853061
## 25 B 0.1970896749
## 26 M 0.7689463221
## 27 B 0.7914438541
## 28 M 0.7290744455
## 29 B 0.1631383158
## 30 B 0.0761838734
## 31 M 0.7290996044
## 32 M 0.7349402279
## 33 M 0.7290321252
## 34 M 0.7290625416
## 35 B 0.4316680111
## 36 B 0.0306830496
## 37 M 0.1523193270
## 38 B 0.0087966314
## 39 M 0.7251864555
## 40 M 0.7290782595
## 41 M 0.8598266814
## 42 M 0.7290026567
## 43 M 0.9938849286
## 44 B 0.6063179288
## 45 B 0.8035877278
## 46 M 0.6242223973
## 47 M 0.7290799020
## 48 B 0.4563422587
## 49 B 0.2047403876
## 50 B 0.2047985340
## 51 B 0.0265004852
## 52 B 0.1745819827
## 53 M 0.6781421929
## 54 B 0.0098709363
## 55 B 0.0114137185
## 56 B 0.2264681105
## 57 M 0.4824927787
## 58 M 0.8670805402
## 59 M 0.4355184055
## 60 M 0.7289930039
## 61 M 0.9591627888
## 62 B 0.0052187035
## 63 B 0.1841120773
## 64 M 0.6889729612
## 65 B 0.2138211654
## 66 B 0.0824628982
## 67 B 0.0318241383
## 68 B 0.0083108275
## 69 B 0.0018392697
## 70 B 0.1504943078
## 71 B 0.0405801266
## 72 B 0.2869119087
## 73 B 0.0462521616
## 74 B 0.2216940163
## 75 B 0.0674004043
## 76 B 0.2047863777
## 77 B 0.1316489487
## 78 B 0.0221222591
## 79 M 0.7824602486
## 80 B 0.2048291380
## 81 B 0.5441367870
## 82 B 0.2048147283
## 83 B 0.5165131506
## 84 M 0.9132258558
## 85 M 0.7291484662
## 86 B 0.2048362023
## 87 M 0.1806026856
## 88 B 0.0218300552
## 89 B 0.0270350012
## 90 B 0.0320670836
## 91 M 0.9589181012
## 92 B 0.0169817243
## 93 B 0.0265853072
## 94 B 0.0865747861
## 95 B 0.0084736509
## 96 M 0.6498979581
## 97 B 0.0013661124
## 98 B 0.7197280965
## 99 B 0.1097556022
## 100 B 0.1616817543
## 101 M 0.9315351853
## 102 B 0.3177547602
## 103 B 0.2047586547
## 104 B 0.5262180182
## 105 B 0.1875604282
## 106 B 0.7716527296
## 107 B 0.1445524662
## 108 B 0.8269889251
## 109 M 0.3266559858
## 110 M 0.8787591167
## 111 B 0.1809532465
## 112 B 0.4755611353
## 113 B 0.3888383226
## 114 B 0.2843340190
## 115 B 0.1473702752
## 116 B 0.2047528091
## 117 B 0.1067514334
## 118 M 0.9479107742
## 119 M 0.9773435443
## 120 M 0.5754594849
## 121 M 0.6015802796
## 122 B 0.0649192935
## 123 B 0.0213288903
## 124 M 0.9742491322
## 125 M 0.7290773094
## 126 M 0.1669205011
## 127 M 0.9932556308
## 128 B 0.4614325728
## 129 B 0.0275797282
## 130 B 0.0522901184
## 131 M 0.7290866363
## 132 B 0.2047713327
## 133 B 0.1584687810
## 134 M 0.8507838033
## 135 B 0.0068373114
## 136 M 0.7290163932
## 137 B 0.2047251065
## 138 B 0.2047885799
## 139 B 0.7914438541
## 140 M 0.9674604705
## 141 M 0.7289655089
## 142 B 0.0814530866
## 143 M 0.1674733888
## 144 M 0.7633769432
## 145 B 0.2047902442
## 146 B 0.2047931519
## 147 B 0.1856451887
## 148 M 0.6585283243
## 149 M 0.7291168244
## 150 M 0.7704623651
## 151 M 0.8547308285
## 152 M 0.8598266814
## 153 B 0.0993871425
## 154 M 0.5397197215
## 155 M 0.6242223973
## 156 B 0.4563422587
## 157 B 0.0265004852
## 158 B 0.0059930669
## 159 M 0.7890539791
## 160 B 0.1823812336
## 161 B 0.0763185137
## 162 B 0.6154978701
## 163 M 0.8133833089
## 164 M 0.1518747402
## 165 M 0.7995780848
## 166 B 0.2138211654
## 167 B 0.0007057461
## 168 M 0.5019879468
## 169 M 0.7290765956
## 170 B 0.0151907106
## 171 B 0.2047602499
## 172 B 0.0018950016
## 173 B 0.0297726297
## 174 B 0.0346484595
## 175 M 0.3196586472
## 176 B 0.2047513452
## 177 M 0.7719056636
## 178 B 0.0245628151
## 179 B 0.1397775263
## 180 M 0.9219526232
## 181 M 0.7290235658
## 182 B 0.0405801266
## 183 B 0.0881705707
## 184 B 0.2869119087
## 185 M 0.7691243509
## 186 B 0.0016429278
## 187 B 0.2047986697
## 188 B 0.2910240994
## 189 B 0.1316489487
## 190 B 0.1988594824
## 191 B 0.0054014539
## 192 B 0.0026813409
## 193 B 0.0354992607
## 194 B 0.2047453695
## 195 B 0.8763465750
## 196 M 0.9189588302
## 197 B 0.1960193136
## 198 B 0.0928743590
## 199 B 0.6866198710
## 200 B 0.0218300552
## 201 B 0.2047956979
## 202 M 0.7291675790
## 203 B 0.0329259941
## 204 B 0.0133748316
## 205 B 0.1984969871
## 206 B 0.2047046925
## 207 B 0.0555626842
## 208 B 0.0041192726
## 209 B 0.4214333492
## 210 B 0.0239206631
## 211 B 0.7197280965
## 212 M 0.1521024957
## 213 M 0.9315351853
## 214 B 0.0344278876
## 215 B 0.1387945330
## 216 B 0.2047557364
## 217 B 0.5262180182
## 218 B 0.0012688405
## 219 B 0.1231909083
## 220 M 0.5289037277
## 221 M 0.8787591167
## 222 B 0.2048531346
## 223 B 0.1809532465
## 224 B 0.2048034409
## 225 M 0.3484941721
## 226 B 0.2048194280
## BAL_SVM_R_Test_Predicted.B
## 1 0.095577717
## 2 0.270940390
## 3 0.076653165
## 4 0.259582018
## 5 0.205488311
## 6 0.082918034
## 7 0.084397467
## 8 0.833079499
## 9 0.024165897
## 10 0.035285962
## 11 0.006744369
## 12 0.549199859
## 13 0.999668534
## 14 0.097839023
## 15 0.795228330
## 16 0.929278794
## 17 0.676384776
## 18 0.981249266
## 19 0.255369247
## 20 0.457204494
## 21 0.993162689
## 22 0.805206869
## 23 0.474690099
## 24 0.495514694
## 25 0.802910325
## 26 0.231053678
## 27 0.208556146
## 28 0.270925554
## 29 0.836861684
## 30 0.923816127
## 31 0.270900396
## 32 0.265059772
## 33 0.270967875
## 34 0.270937458
## 35 0.568331989
## 36 0.969316950
## 37 0.847680673
## 38 0.991203369
## 39 0.274813545
## 40 0.270921741
## 41 0.140173319
## 42 0.270997343
## 43 0.006115071
## 44 0.393682071
## 45 0.196412272
## 46 0.375777603
## 47 0.270920098
## 48 0.543657741
## 49 0.795259612
## 50 0.795201466
## 51 0.973499515
## 52 0.825418017
## 53 0.321857807
## 54 0.990129064
## 55 0.988586282
## 56 0.773531890
## 57 0.517507221
## 58 0.132919460
## 59 0.564481595
## 60 0.271006996
## 61 0.040837211
## 62 0.994781297
## 63 0.815887923
## 64 0.311027039
## 65 0.786178835
## 66 0.917537102
## 67 0.968175862
## 68 0.991689173
## 69 0.998160730
## 70 0.849505692
## 71 0.959419873
## 72 0.713088091
## 73 0.953747838
## 74 0.778305984
## 75 0.932599596
## 76 0.795213622
## 77 0.868351051
## 78 0.977877741
## 79 0.217539751
## 80 0.795170862
## 81 0.455863213
## 82 0.795185272
## 83 0.483486849
## 84 0.086774144
## 85 0.270851534
## 86 0.795163798
## 87 0.819397314
## 88 0.978169945
## 89 0.972964999
## 90 0.967932916
## 91 0.041081899
## 92 0.983018276
## 93 0.973414693
## 94 0.913425214
## 95 0.991526349
## 96 0.350102042
## 97 0.998633888
## 98 0.280271903
## 99 0.890244398
## 100 0.838318246
## 101 0.068464815
## 102 0.682245240
## 103 0.795241345
## 104 0.473781982
## 105 0.812439572
## 106 0.228347270
## 107 0.855447534
## 108 0.173011075
## 109 0.673344014
## 110 0.121240883
## 111 0.819046753
## 112 0.524438865
## 113 0.611161677
## 114 0.715665981
## 115 0.852629725
## 116 0.795247191
## 117 0.893248567
## 118 0.052089226
## 119 0.022656456
## 120 0.424540515
## 121 0.398419720
## 122 0.935080706
## 123 0.978671110
## 124 0.025750868
## 125 0.270922691
## 126 0.833079499
## 127 0.006744369
## 128 0.538567427
## 129 0.972420272
## 130 0.947709882
## 131 0.270913364
## 132 0.795228667
## 133 0.841531219
## 134 0.149216197
## 135 0.993162689
## 136 0.270983607
## 137 0.795274894
## 138 0.795211420
## 139 0.208556146
## 140 0.032539529
## 141 0.271034491
## 142 0.918546913
## 143 0.832526611
## 144 0.236623057
## 145 0.795209756
## 146 0.795206848
## 147 0.814354811
## 148 0.341471676
## 149 0.270883176
## 150 0.229537635
## 151 0.145269171
## 152 0.140173319
## 153 0.900612857
## 154 0.460280278
## 155 0.375777603
## 156 0.543657741
## 157 0.973499515
## 158 0.994006933
## 159 0.210946021
## 160 0.817618766
## 161 0.923681486
## 162 0.384502130
## 163 0.186616691
## 164 0.848125260
## 165 0.200421915
## 166 0.786178835
## 167 0.999294254
## 168 0.498012053
## 169 0.270923404
## 170 0.984809289
## 171 0.795239750
## 172 0.998104998
## 173 0.970227370
## 174 0.965351540
## 175 0.680341353
## 176 0.795248655
## 177 0.228094336
## 178 0.975437185
## 179 0.860222474
## 180 0.078047377
## 181 0.270976434
## 182 0.959419873
## 183 0.911829429
## 184 0.713088091
## 185 0.230875649
## 186 0.998357072
## 187 0.795201330
## 188 0.708975901
## 189 0.868351051
## 190 0.801140518
## 191 0.994598546
## 192 0.997318659
## 193 0.964500739
## 194 0.795254631
## 195 0.123653425
## 196 0.081041170
## 197 0.803980686
## 198 0.907125641
## 199 0.313380129
## 200 0.978169945
## 201 0.795204302
## 202 0.270832421
## 203 0.967074006
## 204 0.986625168
## 205 0.801503013
## 206 0.795295308
## 207 0.944437316
## 208 0.995880727
## 209 0.578566651
## 210 0.976079337
## 211 0.280271903
## 212 0.847897504
## 213 0.068464815
## 214 0.965572112
## 215 0.861205467
## 216 0.795244264
## 217 0.473781982
## 218 0.998731159
## 219 0.876809092
## 220 0.471096272
## 221 0.121240883
## 222 0.795146865
## 223 0.819046753
## 224 0.795196559
## 225 0.651505828
## 226 0.795180572##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_SVM_R_Test_ROC <- roc(response = BAL_SVM_R_Test$BAL_SVM_R_Test_Observed,
predictor = BAL_SVM_R_Test$BAL_SVM_R_Test_Predicted.M,
levels = rev(levels(BAL_SVM_R_Test$BAL_SVM_R_Test_Observed)))
(BAL_SVM_R_Test_AUROC <- auc(BAL_SVM_R_Test_ROC)[1])## [1] 0.91591211.7.4 Base Learner Model Development using K-Nearest Neighbors (BAL_KNN)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
KNN_Grid = data.frame(k = 1:15)
##################################
# Running the k-nearest neighbors model
# by setting the caret method to 'knn'
##################################
set.seed(12345678)
BAL_KNN_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "knn",
preProc = c("center", "scale"),
tuneGrid = KNN_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_KNN_Tune## k-Nearest Neighbors
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## k ROC Sens Spec
## 1 0.8999215 0.8841176 0.9157254
## 2 0.8810826 0.7476471 0.8335652
## 3 0.8881622 0.7223529 0.8440793
## 4 0.8843389 0.7264706 0.8751670
## 5 0.8801288 0.7294118 0.8877712
## 6 0.8822927 0.7111765 0.8800671
## 7 0.8826117 0.7105882 0.8828680
## 8 0.8824661 0.7176471 0.8814737
## 9 0.8822659 0.7141176 0.8811106
## 10 0.8835703 0.6958824 0.8779588
## 11 0.8866311 0.7100000 0.8793593
## 12 0.8871688 0.7094118 0.8790175
## 13 0.8888748 0.7094118 0.8776171
## 14 0.8887794 0.7105882 0.8797437
## 15 0.8901884 0.7082353 0.8793715
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was k = 1.BAL_KNN_Tune$finalModel## 1-nearest neighbor model
## Training set outcome distribution:
##
## M B
## 340 572BAL_KNN_Tune$results## k ROC Sens Spec ROCSD SensSD SpecSD
## 1 1 0.8999215 0.8841176 0.9157254 0.02896451 0.05993436 0.02526390
## 2 2 0.8810826 0.7476471 0.8335652 0.02389197 0.06191043 0.03572945
## 3 3 0.8881622 0.7223529 0.8440793 0.02482202 0.04827623 0.02869459
## 4 4 0.8843389 0.7264706 0.8751670 0.02288013 0.05453087 0.02753525
## 5 5 0.8801288 0.7294118 0.8877712 0.02422588 0.05076548 0.03244839
## 6 6 0.8822927 0.7111765 0.8800671 0.02770719 0.05434548 0.03295881
## 7 7 0.8826117 0.7105882 0.8828680 0.02576758 0.05497850 0.03623026
## 8 8 0.8824661 0.7176471 0.8814737 0.02787252 0.04611492 0.03152335
## 9 9 0.8822659 0.7141176 0.8811106 0.02617793 0.04494326 0.03184781
## 10 10 0.8835703 0.6958824 0.8779588 0.02477549 0.04131629 0.03514350
## 11 11 0.8866311 0.7100000 0.8793593 0.02563095 0.04213691 0.03419137
## 12 12 0.8871688 0.7094118 0.8790175 0.02675550 0.03707710 0.03037541
## 13 13 0.8888748 0.7094118 0.8776171 0.02545936 0.03756003 0.03167993
## 14 14 0.8887794 0.7105882 0.8797437 0.02563345 0.03976031 0.03542890
## 15 15 0.8901884 0.7082353 0.8793715 0.02412346 0.04259632 0.03291041(BAL_KNN_Train_AUROC <- BAL_KNN_Tune$results[BAL_KNN_Tune$results$k==BAL_KNN_Tune$bestTune$k,
c("ROC")])## [1] 0.8999215##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_KNN_Test <- data.frame(BAL_KNN_Test_Observed = MA_Test$diagnosis,
BAL_KNN_Test_Predicted = predict(BAL_KNN_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_KNN_Test## BAL_KNN_Test_Observed BAL_KNN_Test_Predicted.M BAL_KNN_Test_Predicted.B
## 1 M 1 0
## 2 M 1 0
## 3 M 1 0
## 4 M 1 0
## 5 M 1 0
## 6 M 1 0
## 7 M 1 0
## 8 M 1 0
## 9 M 1 0
## 10 M 1 0
## 11 M 1 0
## 12 M 1 0
## 13 B 0 1
## 14 M 1 0
## 15 B 0 1
## 16 B 0 1
## 17 M 1 0
## 18 B 0 1
## 19 B 0 1
## 20 B 0 1
## 21 B 0 1
## 22 B 0 1
## 23 B 0 1
## 24 B 0 1
## 25 B 0 1
## 26 M 1 0
## 27 B 1 0
## 28 M 1 0
## 29 B 0 1
## 30 B 0 1
## 31 M 1 0
## 32 M 1 0
## 33 M 1 0
## 34 M 1 0
## 35 B 0 1
## 36 B 0 1
## 37 M 1 0
## 38 B 0 1
## 39 M 1 0
## 40 M 1 0
## 41 M 1 0
## 42 M 1 0
## 43 M 1 0
## 44 B 0 1
## 45 B 0 1
## 46 M 1 0
## 47 M 1 0
## 48 B 1 0
## 49 B 0 1
## 50 B 0 1
## 51 B 0 1
## 52 B 0 1
## 53 M 1 0
## 54 B 0 1
## 55 B 0 1
## 56 B 0 1
## 57 M 1 0
## 58 M 1 0
## 59 M 1 0
## 60 M 1 0
## 61 M 1 0
## 62 B 0 1
## 63 B 0 1
## 64 M 1 0
## 65 B 0 1
## 66 B 0 1
## 67 B 0 1
## 68 B 0 1
## 69 B 0 1
## 70 B 0 1
## 71 B 0 1
## 72 B 0 1
## 73 B 0 1
## 74 B 0 1
## 75 B 0 1
## 76 B 0 1
## 77 B 0 1
## 78 B 0 1
## 79 M 1 0
## 80 B 0 1
## 81 B 0 1
## 82 B 0 1
## 83 B 0 1
## 84 M 1 0
## 85 M 1 0
## 86 B 0 1
## 87 M 1 0
## 88 B 0 1
## 89 B 0 1
## 90 B 0 1
## 91 M 1 0
## 92 B 0 1
## 93 B 0 1
## 94 B 0 1
## 95 B 0 1
## 96 M 1 0
## 97 B 0 1
## 98 B 1 0
## 99 B 0 1
## 100 B 0 1
## 101 M 1 0
## 102 B 0 1
## 103 B 0 1
## 104 B 1 0
## 105 B 0 1
## 106 B 0 1
## 107 B 0 1
## 108 B 0 1
## 109 M 1 0
## 110 M 1 0
## 111 B 0 1
## 112 B 0 1
## 113 B 0 1
## 114 B 0 1
## 115 B 0 1
## 116 B 0 1
## 117 B 0 1
## 118 M 1 0
## 119 M 1 0
## 120 M 1 0
## 121 M 1 0
## 122 B 0 1
## 123 B 0 1
## 124 M 1 0
## 125 M 1 0
## 126 M 1 0
## 127 M 1 0
## 128 B 0 1
## 129 B 0 1
## 130 B 0 1
## 131 M 1 0
## 132 B 0 1
## 133 B 0 1
## 134 M 1 0
## 135 B 0 1
## 136 M 1 0
## 137 B 0 1
## 138 B 0 1
## 139 B 1 0
## 140 M 1 0
## 141 M 1 0
## 142 B 0 1
## 143 M 1 0
## 144 M 1 0
## 145 B 0 1
## 146 B 0 1
## 147 B 0 1
## 148 M 1 0
## 149 M 1 0
## 150 M 1 0
## 151 M 1 0
## 152 M 1 0
## 153 B 0 1
## 154 M 1 0
## 155 M 1 0
## 156 B 1 0
## 157 B 0 1
## 158 B 0 1
## 159 M 1 0
## 160 B 0 1
## 161 B 0 1
## 162 B 0 1
## 163 M 1 0
## 164 M 1 0
## 165 M 1 0
## 166 B 0 1
## 167 B 0 1
## 168 M 1 0
## 169 M 1 0
## 170 B 0 1
## 171 B 0 1
## 172 B 0 1
## 173 B 0 1
## 174 B 0 1
## 175 M 1 0
## 176 B 0 1
## 177 M 1 0
## 178 B 0 1
## 179 B 0 1
## 180 M 1 0
## 181 M 1 0
## 182 B 0 1
## 183 B 0 1
## 184 B 0 1
## 185 M 1 0
## 186 B 0 1
## 187 B 0 1
## 188 B 0 1
## 189 B 0 1
## 190 B 0 1
## 191 B 0 1
## 192 B 0 1
## 193 B 0 1
## 194 B 0 1
## 195 B 0 1
## 196 M 1 0
## 197 B 0 1
## 198 B 0 1
## 199 B 0 1
## 200 B 0 1
## 201 B 0 1
## 202 M 1 0
## 203 B 0 1
## 204 B 0 1
## 205 B 0 1
## 206 B 0 1
## 207 B 0 1
## 208 B 0 1
## 209 B 0 1
## 210 B 0 1
## 211 B 1 0
## 212 M 1 0
## 213 M 1 0
## 214 B 0 1
## 215 B 0 1
## 216 B 0 1
## 217 B 1 0
## 218 B 0 1
## 219 B 0 1
## 220 M 1 0
## 221 M 1 0
## 222 B 0 1
## 223 B 0 1
## 224 B 0 1
## 225 M 1 0
## 226 B 0 1##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_KNN_Test_ROC <- roc(response = BAL_KNN_Test$BAL_KNN_Test_Observed,
predictor = BAL_KNN_Test$BAL_KNN_Test_Predicted.M,
levels = rev(levels(BAL_KNN_Test$BAL_KNN_Test_Observed)))
(BAL_KNN_Test_AUROC <- auc(BAL_KNN_Test_ROC)[1])## [1] 0.9718311.7.5 Base Learner Model Development using Naive Bayes (BAL_NB)
Details.
Code Chunk | Output
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
NB_Grid = data.frame(usekernel = c(TRUE, FALSE),
fL = 2,
adjust = FALSE)
##################################
# Running the naive bayes model
# by setting the caret method to 'nb'
##################################
set.seed(12345678)
BAL_NB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "nb",
tuneGrid = NB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_NB_Tune## Naive Bayes
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## usekernel ROC Sens Spec
## FALSE 0.8864212 0.7576471 0.8643356
## TRUE NaN NaN NaN
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were fL = 2, usekernel = FALSE and adjust
## = FALSE.BAL_NB_Tune$finalModel## $apriori
## grouping
## M B
## 0.372807 0.627193
##
## $tables
## $tables$texture_mean
## [,1] [,2]
## M 3.053171 0.1768317
## B 2.861218 0.2118987
##
## $tables$smoothness_mean
## [,1] [,2]
## M -2.283184 0.1220104
## B -2.392952 0.1412754
##
## $tables$compactness_se
## [,1] [,2]
## M -3.579629 0.5293692
## B -4.063368 0.6656621
##
## $tables$texture_worst
## [,1] [,2]
## M 4.790170 0.3648083
## B 4.360104 0.4212065
##
## $tables$smoothness_worst
## [,1] [,2]
## M -1.471766 0.08336974
## B -1.553719 0.08524520
##
## $tables$symmetry_worst
## [,1] [,2]
## M -1.588074 0.4012040
## B -1.881098 0.3039037
##
##
## $levels
## [1] "M" "B"
##
## $call
## NaiveBayes.default(x = x, grouping = y, usekernel = FALSE, fL = param$fL)
##
## $x
## texture_mean smoothness_mean compactness_se texture_worst
## X1 2.339881 -2.133687 -3.015119 3.845649
## X2 2.877512 -2.468168 -4.336671 4.393994
## X3 3.056357 -2.210918 -3.217377 4.558289
## X4 3.014554 -1.948413 -2.595883 4.629842
## X5 2.663053 -2.299590 -3.704602 3.777223
## X6 2.753661 -2.057289 -3.397703 4.421124
## X7 2.994732 -2.357781 -4.281638 4.712710
## X9 3.082827 -2.061209 -3.351836 4.919334
## X10 3.179719 -2.131999 -2.628731 5.491708
## X11 3.145875 -2.500305 -4.681080 5.114832
## X12 2.884242 -2.332014 -3.203741 4.685875
## X14 3.175968 -2.476819 -3.465416 4.712710
## X15 3.118392 -2.179483 -2.824135 5.000625
## X17 3.002211 -2.315974 -4.455028 4.928999
## X18 3.029167 -2.145581 -3.688480 4.967287
## X20 2.664447 -2.324933 -4.226734 4.034440
## X21 2.754297 -2.230264 -3.964369 4.146994
## X22 2.520917 -2.278869 -4.246098 3.668189
## X23 2.657458 -2.232127 -2.932194 4.017490
## X25 3.062456 -2.188364 -3.972835 4.972347
## X26 2.797281 -2.131999 -3.270432 4.226835
## X27 3.069447 -2.249993 -3.488391 5.074506
## X28 3.008155 -2.360214 -3.603803 4.684455
## X29 3.229618 -2.223774 -3.487736 5.278432
## X30 2.711378 -2.318003 -3.495618 4.058702
## X32 2.928524 -2.199126 -3.377286 4.744803
## X33 3.177220 -2.122767 -3.479591 5.005619
## X34 3.276012 -2.364354 -3.405808 4.930285
## X35 2.883683 -2.263364 -3.551555 4.684455
## X36 3.072230 -2.342366 -3.689280 4.806397
## X38 2.913437 -2.409836 -5.318724 4.345339
## X39 3.226844 -2.365844 -4.515329 4.533450
## X40 3.035914 -2.286712 -3.799141 4.594701
## X41 3.071767 -2.505681 -4.508043 4.888151
## X43 3.211247 -2.398986 -2.296603 5.072078
## X45 3.082369 -2.331602 -4.280192 4.864503
## X47 2.823757 -2.453408 -4.106822 4.274627
## X49 2.683074 -2.272056 -4.249596 4.165667
## X50 3.104587 -2.435888 -4.281638 4.988725
## X51 3.072693 -2.449115 -4.629668 4.572474
## X52 2.793616 -2.565900 -4.442201 4.376271
## X53 2.903617 -2.493625 -4.781907 4.220791
## X55 3.091951 -2.401743 -4.575611 4.980549
## X56 2.931194 -2.351355 -4.741907 4.317312
## X57 2.921547 -2.250942 -3.769656 4.746189
## X58 3.072230 -2.174192 -3.556098 4.917397
## X59 2.960623 -2.518257 -4.756807 4.298995
## X60 2.467252 -2.327698 -4.551629 3.639212
## X62 3.043570 -2.085057 -3.453965 4.668773
## X63 3.097837 -2.254748 -2.651292 4.839292
## X64 2.629007 -2.561226 -3.234497 4.031624
## X66 3.175551 -2.143873 -3.767923 5.085404
## X67 3.044999 -2.259526 -4.042701 4.972347
## X68 2.946542 -2.508503 -4.686814 4.428254
## X73 3.199489 -2.233992 -2.879551 5.111247
## X74 2.759377 -2.295609 -3.880040 4.179793
## X75 2.804572 -2.389015 -4.006883 4.377888
## X76 2.978077 -2.389451 -3.815350 4.484527
## X77 2.392426 -2.047168 -3.561718 3.284809
## X78 2.781920 -2.239610 -2.840611 4.001364
## X79 3.176803 -2.051048 -2.683114 4.982438
## X80 2.890372 -2.309207 -4.097750 4.504524
## X81 3.043093 -2.205458 -4.071019 5.009980
## X83 3.215269 -2.241490 -2.865933 5.099260
## X84 3.269189 -2.107841 -2.805112 5.044600
## X85 2.750471 -2.330676 -4.010739 4.510643
## X86 2.918851 -2.315265 -4.105001 4.714115
## X87 3.066191 -2.359791 -3.512241 4.821893
## X88 3.202340 -2.404729 -3.994318 4.898589
## X90 2.723924 -2.178599 -3.120842 3.936655
## X91 3.178887 -2.410839 -4.007433 4.812472
## X92 3.125005 -2.385967 -3.711534 4.581390
## X94 2.906901 -2.280824 -4.205723 4.588794
## X95 2.987196 -2.264326 -3.292792 4.458901
## X96 3.136798 -2.399316 -3.357563 4.974243
## X97 2.881443 -2.258568 -4.440504 4.185067
## X99 2.552565 -2.409836 -4.319991 3.828226
## X100 2.984166 -2.327698 -3.542185 4.927712
## X101 3.218076 -2.355142 -4.207737 5.196499
## X102 2.597491 -2.145581 -4.524512 4.060557
## X105 2.959587 -2.303686 -3.808114 4.385955
## X106 2.744704 -1.967542 -3.536330 4.311499
## X108 2.919931 -2.467814 -4.559241 4.700742
## X110 3.056827 -2.435088 -4.162409 4.815168
## X111 2.832625 -2.266253 -3.529485 4.232863
## X112 3.033028 -2.309308 -3.208431 4.553792
## X113 2.978077 -2.546314 -2.597493 4.419537
## X114 3.005187 -2.187472 -3.284215 4.340417
## X115 2.761907 -2.162823 -3.810821 4.067964
## X117 2.757475 -2.357886 -2.694147 3.818947
## X118 2.813611 -2.152442 -3.663992 4.692258
## X119 3.131573 -2.158485 -3.224894 4.904441
## X120 2.996232 -2.476700 -4.776908 4.724620
## X121 2.381396 -2.367337 -4.180556 3.702239
## X122 2.840247 -2.249993 -3.862757 4.510643
## X124 2.387845 -2.206366 -4.361440 3.703328
## X126 2.845491 -2.432124 -4.691927 4.407598
## X127 3.206398 -2.379682 -4.444753 5.217803
## X128 2.939691 -2.498965 -3.600502 4.573218
## X131 2.587012 -2.238672 -3.706636 3.894116
## X132 2.969388 -2.214574 -4.210429 4.593226
## X133 3.069912 -2.294617 -3.936316 4.979920
## X134 2.634045 -2.357886 -4.189755 4.033502
## X135 3.086943 -2.361274 -4.253106 4.961581
## X137 2.813611 -2.252843 -4.283087 4.554542
## X138 2.733718 -2.339353 -4.185802 4.279690
## X140 2.594508 -2.150723 -3.352979 3.680332
## X141 2.482404 -2.380547 -5.175038 3.488165
## X142 2.893146 -2.330882 -3.943514 4.538741
## X143 2.851284 -2.214574 -4.054163 4.648665
## X144 2.767576 -2.444494 -4.158563 4.262772
## X145 2.706048 -2.551944 -4.027995 4.167437
## X146 2.684440 -2.161086 -3.048922 3.760309
## X148 2.932260 -2.508626 -3.020640 4.553792
## X151 3.033991 -2.175952 -4.472389 4.449513
## X152 3.030134 -2.363929 -2.915813 4.853256
## X153 2.730464 -2.233059 -2.344866 4.055916
## X154 2.571084 -2.327493 -4.712199 3.737909
## X155 2.730464 -2.366164 -3.903559 4.147887
## X156 2.887033 -2.447149 -4.159203 4.534963
## X157 3.032064 -2.193731 -3.004975 4.526631
## X158 2.968361 -2.597628 -3.626468 4.741335
## X159 2.544747 -2.373974 -4.626496 3.953251
## X160 2.561868 -2.588269 -5.093908 3.932732
## X161 3.004692 -2.217325 -3.727620 4.608673
## X163 2.898671 -2.189256 -3.782311 4.621834
## X164 3.100993 -2.290657 -3.449863 4.783310
## X165 3.092859 -2.472306 -3.671433 4.751724
## X166 2.983660 -2.474442 -4.830441 4.579906
## X168 2.933857 -2.423059 -3.700952 4.615263
## X169 3.205993 -2.254748 -3.314836 5.020540
## X170 2.830268 -2.317191 -4.290359 4.360856
## X171 2.516890 -2.274970 -4.439656 3.665973
## X172 2.977059 -2.402626 -4.544075 4.863182
## X173 2.475698 -2.073857 -3.763172 3.815845
## X174 2.688528 -2.296603 -3.853283 3.792962
## X175 2.718001 -2.431328 -4.588313 4.028805
## X176 2.670694 -2.392729 -4.827439 3.815845
## X177 2.893700 -2.333147 -2.429510 4.471360
## X178 3.001217 -2.319630 -3.195648 4.767568
## X179 3.100993 -2.772429 -6.095937 4.806397
## X180 2.569554 -2.437374 -5.057098 3.721768
## X181 3.085116 -2.212744 -3.674188 5.052569
## X182 3.279783 -2.170680 -3.045133 5.090835
## X183 3.011113 -2.343720 -3.788479 5.050733
## X184 2.702703 -2.401411 -3.289298 3.883102
## X186 2.715357 -2.378710 -4.422849 4.207786
## X187 2.922086 -2.454804 -4.690619 4.619646
## X188 2.844328 -2.325444 -4.743973 4.225973
## X189 2.855895 -2.295609 -4.735735 4.628388
## X190 2.766319 -2.515778 -3.811273 4.065190
## X192 3.063858 -2.436231 -4.022955 4.401206
## X193 2.902520 -2.666429 -5.000289 4.177151
## X195 3.144583 -2.259526 -2.971625 4.721123
## X196 2.793004 -2.533131 -4.143325 4.278004
## X198 3.083743 -2.607617 -2.938218 4.495315
## X199 3.113071 -2.462402 -3.297378 5.003747
## X202 2.961141 -2.411508 -3.661653 4.581390
## X203 3.283539 -2.170680 -2.971820 5.042143
## X204 3.167583 -2.022683 -3.471191 5.551376
## X205 2.923162 -2.306091 -3.957544 4.490698
## X206 2.814210 -2.421819 -3.953366 4.124564
## X207 2.848971 -2.217325 -4.382827 4.378696
## X208 3.008648 -2.433605 -4.197707 4.522074
## X210 2.558002 -2.503234 -4.393290 3.696783
## X212 2.941276 -2.422383 -4.058784 4.517508
## X214 3.241029 -2.296603 -2.458654 4.741335
## X215 3.170106 -2.357781 -3.294138 5.172099
## X216 2.829087 -2.276917 -3.370280 4.660893
## X218 2.861057 -2.519001 -3.467337 4.477566
## X219 3.070840 -2.366271 -3.451754 4.780580
## X220 3.480317 -2.474560 -3.632877 5.725074
## X224 3.008155 -2.277892 -3.740594 4.890764
## X225 2.834389 -2.471596 -4.506230 4.409194
## X226 2.600465 -2.312030 -4.248895 3.801311
## X227 2.738256 -2.250942 -4.820718 4.084542
## X229 3.176803 -2.537928 -3.774873 4.956498
## X230 3.105931 -2.218244 -3.255021 4.881604
## X231 2.948641 -2.170680 -3.908031 4.509879
## X232 3.298795 -2.676116 -4.106215 5.107058
## X233 3.520757 -2.553614 -4.988923 5.547844
## X234 3.325396 -2.390433 -3.881494 5.315680
## X235 2.766948 -2.469348 -4.565949 4.025039
## X236 3.056357 -2.400198 -4.280915 4.890111
## X238 3.066191 -2.482310 -3.463179 4.605738
## X240 3.670715 -2.321564 -3.580922 5.699444
## X241 2.747271 -2.362017 -4.383628 4.014653
## X243 2.900872 -2.344241 -2.827848 4.733688
## X244 3.168424 -2.520368 -3.624216 4.618186
## X245 3.157000 -2.275943 -3.425900 4.906389
## X247 2.858193 -2.629008 -4.154732 4.723921
## X248 2.646884 -2.434974 -2.883833 3.883102
## X249 3.227637 -2.337487 -4.570769 5.191870
## X250 2.703373 -2.289669 -4.399783 4.208655
## X251 3.159550 -2.295609 -3.242144 4.665910
## X252 2.915064 -2.370329 -4.542195 4.316482
## X253 2.986692 -2.242431 -2.984397 4.562778
## X254 2.837908 -2.294617 -4.197707 4.525113
## X255 2.961658 -2.268184 -4.017384 4.485299
## X257 3.359333 -2.379466 -3.043873 5.253674
## X258 2.848971 -2.013654 -3.081726 4.333015
## X260 3.513335 -2.241490 -3.666727 5.913428
## X261 3.298057 -2.302585 -4.149012 5.412105
## X264 2.964242 -2.545931 -4.698932 4.979289
## X265 3.094219 -2.330367 -4.417861 4.827259
## X267 2.941804 -2.334282 -3.329528 4.355967
## X268 3.083743 -2.531244 -3.727205 4.874383
## X269 2.785628 -2.361804 -3.826763 4.412381
## X270 3.015045 -2.223774 -3.039684 4.534207
## X271 2.822569 -2.744351 -5.596723 4.161235
## X273 3.044046 -2.364354 -3.003764 4.748958
## X276 2.854169 -2.099644 -4.207065 4.008967
## X277 2.650421 -2.366697 -4.710753 4.008967
## X278 2.994732 -2.416538 -4.497213 4.464360
## X279 2.881443 -2.532250 -5.244966 4.600594
## X280 2.719979 -2.352196 -4.172739 4.255969
## X281 3.280911 -2.282782 -3.709490 5.232668
## X282 2.640485 -2.549381 -4.239139 3.938613
## X283 2.900322 -2.266253 -3.817167 4.781263
## X284 2.932260 -2.238672 -3.357851 4.525113
## X286 2.912351 -2.477772 -4.827314 4.367359
## X287 3.033028 -2.452827 -2.965009 4.686585
## X288 2.574138 -2.665709 -4.308776 3.654863
## X289 2.993730 -2.523232 -2.493503 4.305672
## X290 2.938633 -2.440354 -4.272276 4.603535
## X291 2.982140 -2.435317 -2.240550 4.288943
## X292 2.949688 -2.408835 -3.856115 4.607206
## X293 2.773838 -2.297598 -3.910524 4.096440
## X294 2.859913 -2.480277 -4.199705 4.574706
## X295 2.623218 -2.336452 -4.439656 3.860909
## X296 2.585506 -2.386184 -4.809369 3.804433
## X297 2.513656 -2.462989 -4.405500 3.573135
## X298 2.898119 -2.305790 -4.600183 4.392389
## X299 2.899772 -2.721744 -4.285263 4.537986
## X301 2.939162 -2.162823 -3.441082 4.610871
## X302 2.990217 -2.470885 -3.390554 4.366547
## X303 3.172203 -2.225624 -3.050822 4.833951
## X304 2.923699 -2.236797 -4.671096 4.482982
## X305 2.899221 -2.424414 -3.629856 4.244873
## X306 3.198265 -2.593740 -3.756302 4.976136
## X307 2.761275 -2.463811 -4.845841 4.143420
## X309 2.542389 -2.606939 -5.587067 3.805473
## X310 2.627563 -2.482669 -5.357855 3.852784
## X311 2.950212 -2.428829 -4.698383 4.633474
## X312 2.753024 -2.574656 -5.112502 4.256821
## X313 2.593013 -2.431101 -3.587045 3.748604
## X314 2.372111 -2.453757 -4.312501 3.334618
## X315 2.923162 -2.231195 -4.266557 4.314822
## X316 2.824351 -2.463811 -5.805151 4.076268
## X317 2.644755 -2.559544 -5.155603 3.756060
## X318 2.937573 -2.328313 -4.098955 4.518270
## X319 2.939162 -2.305790 -2.719617 4.393191
## X320 2.833213 -2.582696 -4.529135 4.121857
## X321 2.783776 -2.243373 -3.154728 4.157683
## X322 2.978586 -2.523232 -4.285989 4.363297
## X323 2.589267 -2.176834 -3.904055 4.199074
## X324 3.068518 -2.145581 -3.716867 4.991235
## X325 2.721953 -2.444955 -4.255923 4.225110
## X326 2.850707 -2.274970 -4.654991 4.200819
## X327 2.555676 -2.374189 -4.481184 3.913012
## X329 3.030617 -2.146436 -3.871361 4.896635
## X330 3.085573 -2.149864 -3.281816 4.534207
## X331 2.741485 -2.354826 -3.428055 4.276316
## X332 2.962692 -2.345597 -3.424978 4.273783
## X333 2.988708 -2.249993 -4.506230 4.576936
## X334 2.693275 -2.488192 -4.944286 4.283059
## X335 2.945491 -2.487350 -4.775721 4.768255
## X336 3.044522 -2.190150 -4.019052 5.070864
## X337 2.655352 -2.357886 -3.770090 3.802352
## X338 3.064792 -2.395139 -3.244963 5.143922
## X339 2.863914 -2.295609 -4.234297 4.654427
## X340 3.189241 -2.235861 -3.926629 4.919334
## X341 2.805782 -2.327800 -3.489045 4.236301
## X342 2.823757 -2.467342 -3.360727 4.366547
## X343 2.705380 -2.270118 -3.975495 4.093700
## X344 3.076390 -2.323094 -3.390851 5.160983
## X346 2.688528 -2.314455 -3.063797 4.054986
## X347 2.939162 -2.478607 -4.513503 4.670202
## X348 2.690565 -2.421932 -4.195713 3.906069
## X350 2.705380 -2.155891 -3.722643 3.885109
## X351 2.837323 -2.582167 -4.963132 4.079030
## X352 2.955951 -2.085057 -2.724332 4.454212
## X353 2.859913 -2.163693 -3.159900 4.407598
## X354 3.248046 -2.278869 -3.716867 5.075113
## X355 2.644045 -2.620864 -3.385226 3.685830
## X357 2.922624 -2.223774 -3.236022 4.506820
## X358 2.785628 -2.436917 -4.835968 4.562030
## X359 2.740195 -2.489758 -3.540804 3.883102
## X360 2.907993 -2.293625 -4.712533 4.519792
## X361 2.894253 -2.598837 -5.361683 4.190330
## X362 3.071303 -2.455503 -3.889772 4.818532
## X364 2.906354 -2.334489 -4.014610 4.552291
## X366 3.080992 -2.391416 -3.960163 4.620376
## X367 3.289521 -2.312131 -3.063155 5.110649
## X369 2.847812 -2.366164 -4.499010 4.625478
## X370 3.086487 -2.241490 -3.568079 4.578422
## X371 3.148024 -2.328724 -3.239844 4.938626
## X372 2.580974 -2.530364 -4.209755 3.675924
## X373 2.714695 -2.301586 -3.621221 4.264469
## X374 2.853593 -2.359579 -3.965951 4.374653
## X375 2.776954 -2.488674 -4.143325 4.121857
## X376 2.776954 -2.314658 -4.010739 4.023154
## X377 3.006672 -2.399867 -2.571380 4.346158
## X378 3.339677 -2.588003 -4.420352 5.217230
## X379 2.718001 -2.492778 -3.511906 4.069812
## X380 2.935451 -2.107018 -3.090263 5.050733
## X381 2.561868 -2.089896 -4.030244 4.150563
## X384 2.861057 -2.261443 -3.295487 4.371414
## X385 2.618855 -2.481353 -3.876173 3.849729
## X386 3.148024 -2.443918 -4.050136 4.981809
## X389 2.740840 -2.481114 -2.707700 4.003267
## X390 3.144583 -2.292635 -3.194915 4.900541
## X391 2.503074 -2.302985 -4.294016 3.667081
## X392 2.823757 -2.264326 -3.929169 4.344519
## X393 2.994231 -2.154165 -3.530851 4.832614
## X394 3.103689 -2.148149 -3.289835 4.787400
## X395 2.874694 -2.273998 -4.115977 4.578422
## X396 2.843746 -2.520119 -4.363794 4.544020
## X397 2.938633 -2.245260 -3.933757 4.680188
## X398 2.859913 -2.520244 -3.359000 4.197328
## X399 2.696652 -2.558639 -4.220588 4.134460
## X400 2.848392 -2.398325 -3.930187 4.479114
## X402 2.389680 -2.422270 -4.419521 4.115529
## X404 2.783158 -2.314759 -4.354411 4.362484
## X405 2.704711 -2.443918 -4.768748 3.796097
## X406 2.922624 -2.298593 -3.847172 4.562030
## X407 2.698673 -2.354405 -4.385232 4.064264
## X408 3.061988 -2.583490 -3.105547 4.666626
## X409 3.028199 -2.267218 -3.585601 4.549287
## X410 2.885917 -2.443573 -3.767923 4.796917
## X411 2.866193 -2.423849 -4.610484 5.256500
## X412 2.823163 -2.228406 -4.671844 4.625478
## X413 3.076390 -2.529611 -3.621595 4.735776
## X414 3.096030 -2.463341 -3.572698 4.971715
## X415 3.394844 -2.486508 -4.249596 5.289608
## X416 3.052585 -2.325547 -3.489045 4.680900
## X418 3.048325 -2.189256 -3.247018 4.712008
## X419 2.498974 -2.432124 -4.371680 3.803393
## X421 2.946542 -2.459707 -3.888795 4.664478
## X422 2.637628 -2.272056 -2.948086 3.946432
## X423 2.773838 -2.218244 -3.909526 4.072581
## X424 2.951258 -2.401632 -3.572342 4.556042
## X425 2.950735 -2.230264 -3.971242 4.374653
## X426 3.057768 -2.511210 -5.167816 4.800985
## X427 2.706716 -2.321156 -3.252691 4.241448
## X429 2.810607 -2.507030 -5.073096 4.129067
## X430 2.871868 -2.538814 -4.444753 4.188577
## X431 3.114848 -2.307899 -2.778526 4.706382
## X432 2.872434 -2.249993 -3.412764 4.353519
## X433 2.972464 -2.177716 -3.606378 4.523594
## X435 2.829678 -2.416426 -4.095345 4.151454
## X436 2.976549 -2.244316 -4.022396 4.923849
## X437 2.972464 -2.392948 -4.296216 4.470584
## X438 2.771338 -2.470057 -4.346659 4.242305
## X439 2.975530 -2.443688 -4.514416 4.737167
## X440 2.751110 -2.529988 -4.684430 4.039126
## X441 2.844909 -2.417435 -3.070887 4.656584
## X443 2.759377 -2.428489 -4.460204 3.862936
## X446 3.214466 -2.273026 -3.899600 4.895332
## X447 3.333275 -2.302885 -3.904551 5.378924
## X448 2.871302 -2.388252 -4.447312 4.339596
## X449 2.962175 -2.478368 -3.888306 4.763445
## X450 3.021400 -2.334695 -3.875209 5.004371
## X451 3.069912 -2.716133 -2.802965 4.748958
## X452 3.218876 -2.271086 -3.948168 4.934138
## X453 3.340385 -2.472543 -3.653898 5.343130
## X456 3.424914 -2.381087 -4.385232 5.539246
## X457 3.377246 -2.369045 -3.722229 5.393426
## X458 3.228826 -2.431442 -4.818116 5.135645
## X459 3.224062 -2.480636 -4.713424 4.992489
## X461 3.301377 -2.312837 -3.808114 5.150996
## X464 2.910174 -2.464163 -4.073954 4.442448
## X465 2.902520 -2.594811 -4.204383 4.432205
## X466 3.002211 -2.490844 -2.709501 4.556042
## X467 3.032064 -2.444725 -3.448604 4.553042
## X468 2.895912 -2.487590 -4.376442 4.489157
## X469 3.149740 -2.376339 -2.655695 4.796239
## X470 2.900322 -2.141317 -3.440146 4.548535
## X471 2.917230 -2.413964 -3.917538 4.565019
## X473 2.703373 -2.513553 -4.034191 3.934694
## X474 3.400197 -2.564080 -4.798391 5.352397
## X475 2.748552 -2.295609 -3.476029 4.042868
## X476 2.755570 -2.403511 -4.019608 4.042868
## X477 3.021887 -2.415642 -3.414891 4.684455
## X478 2.810607 -2.684138 -4.331334 4.261073
## X479 2.680336 -2.257612 -3.856588 4.269554
## X480 2.970927 -2.276917 -2.905892 4.364923
## X481 2.892037 -2.398325 -4.094745 4.727414
## X482 2.956991 -2.526854 -4.526359 4.624021
## X483 2.643334 -2.233992 -4.232228 3.944480
## X485 2.423031 -2.260484 -4.006334 3.500171
## X486 2.797891 -2.352406 -2.594141 4.194706
## X487 2.824351 -2.448652 -4.273710 4.551541
## X488 2.934920 -2.217325 -3.563834 4.897287
## X490 3.005683 -2.590667 -4.236369 4.634199
## X491 3.110845 -2.502012 -4.418691 4.999375
## X492 2.582487 -2.546186 -4.794637 3.954223
## X495 3.022374 -2.612513 -3.886355 4.768255
## X496 3.006178 -2.344762 -4.289630 4.769627
## X499 2.863343 -2.290657 -3.437654 4.351068
## X500 3.055886 -2.221005 -3.578770 4.921270
## X503 2.792391 -2.155891 -4.156007 4.226835
## X504 2.987196 -2.370650 -3.394420 4.430625
## X505 2.554899 -1.811554 -3.091803 3.746469
## X506 2.575661 -2.075450 -3.018387 3.916970
## X508 2.840247 -2.125276 -3.751606 4.169207
## X510 3.175968 -2.134532 -3.007805 5.257064
## X511 2.687167 -2.513430 -3.240099 3.873042
## X512 2.687847 -2.468404 -4.425352 3.871024
## X513 3.021400 -2.201835 -3.787595 4.849274
## X514 2.614472 -2.319528 -4.010739 3.836443
## X515 2.948116 -2.384338 -4.116590 4.740641
## X516 2.923699 -2.254748 -4.109864 4.363297
## X517 3.024320 -2.236797 -3.975495 4.607940
## X518 3.008648 -2.266253 -3.770958 4.535719
## X519 2.902520 -2.105375 -3.759731 4.469807
## X520 2.815409 -2.184802 -4.159844 4.255969
## X521 2.631889 -1.987045 -3.673006 3.897110
## X522 3.072693 -2.273026 -3.438276 4.660893
## X523 2.987196 -2.463811 -5.132803 4.624750
## X524 2.927453 -2.311021 -3.793796 4.565765
## X528 2.507157 -2.407612 -4.691927 4.035378
## X529 2.577942 -2.081043 -3.427439 3.636967
## X530 2.598235 -2.207275 -4.462803 3.680332
## X533 2.793004 -2.377632 -4.929793 4.120954
## X535 2.869035 -2.334385 -3.387886 4.630569
## X536 3.037833 -2.257612 -3.643524 4.554542
## X538 3.196221 -2.090705 -3.353837 5.011847
## X539 3.238286 -2.513553 -4.636454 4.931570
## X540 3.236323 -2.445532 -2.740005 4.993116
## X541 2.670002 -2.304186 -3.191261 4.073504
## X542 3.218476 -2.426223 -3.067658 4.983068
## X543 3.235536 -2.491931 -4.446458 5.018060
## X545 3.030134 -2.345701 -3.829522 4.499157
## X547 2.794228 -2.360850 -4.927168 4.258523
## X549 2.962175 -2.466163 -4.489167 4.562778
## X552 3.110845 -2.346955 -3.489701 4.754487
## X553 3.382015 -2.491810 -4.395720 5.238363
## X554 3.088311 -2.381628 -3.998671 4.522074
## X555 3.364533 -2.510471 -3.838308 5.223531
## X558 3.327910 -2.510471 -4.488276 5.136237
## X559 3.121483 -2.468286 -3.070671 4.685165
## X560 3.175133 -2.379358 -3.512576 5.303509
## X561 3.301377 -2.309710 -3.620100 5.072078
## X562 3.379974 -2.597090 -4.724179 5.365966
## X563 3.421653 -2.255702 -3.027429 5.598355
## X564 3.222469 -2.208184 -3.144232 4.832614
## X565 3.108614 -2.198225 -3.543568 4.622564
## X566 3.341093 -2.324831 -3.720164 5.363258
## X567 3.335058 -2.470412 -3.288494 5.129122
## X568 3.378611 -2.138767 -2.787418 5.425895
## X569 3.200304 -2.944469 -5.368740 4.895984
## X570 2.339881 -2.133687 -3.015119 3.845649
## X571 2.877512 -2.468168 -4.336671 4.393994
## X572 3.056357 -2.210918 -3.217377 4.558289
## X573 3.014554 -1.948413 -2.595883 4.629842
## X574 2.663053 -2.299590 -3.704602 3.777223
## X576 2.994732 -2.357781 -4.281638 4.712710
## X577 3.036394 -2.129472 -3.496938 4.746189
## X579 3.179719 -2.131999 -2.628731 5.491708
## X580 3.145875 -2.500305 -4.681080 5.114832
## X582 3.210844 -2.328929 -2.489276 4.867801
## X584 3.118392 -2.179483 -2.824135 5.000625
## X585 3.315639 -2.172434 -3.160607 5.301845
## X586 3.002211 -2.315974 -4.455028 4.928999
## X587 3.029167 -2.145581 -3.688480 4.967287
## X588 3.097837 -2.319630 -3.967007 4.928999
## X591 2.520917 -2.278869 -4.246098 3.668189
## X592 2.657458 -2.232127 -2.932194 4.017490
## X593 3.137232 -2.361486 -4.374852 5.214935
## X594 3.062456 -2.188364 -3.972835 4.972347
## X595 2.797281 -2.131999 -3.270432 4.226835
## X596 3.069447 -2.249993 -3.488391 5.074506
## X597 3.008155 -2.360214 -3.603803 4.684455
## X598 3.229618 -2.223774 -3.487736 5.278432
## X599 2.711378 -2.318003 -3.495618 4.058702
## X600 3.223266 -2.240550 -3.389071 5.122583
## X602 3.177220 -2.122767 -3.479591 5.005619
## X604 2.883683 -2.263364 -3.551555 4.684455
## X605 3.072230 -2.342366 -3.689280 4.806397
## X606 3.078233 -2.320444 -3.508226 4.895332
## X607 2.913437 -2.409836 -5.318724 4.345339
## X608 3.226844 -2.365844 -4.515329 4.533450
## X609 3.035914 -2.286712 -3.799141 4.594701
## X610 3.071767 -2.505681 -4.508043 4.888151
## X612 3.211247 -2.398986 -2.296603 5.072078
## X613 3.009635 -2.262403 -3.841099 4.736472
## X614 3.082369 -2.331602 -4.280192 4.864503
## X615 2.867899 -2.208184 -3.220377 4.219926
## X616 2.823757 -2.453408 -4.106822 4.274627
## X618 2.683074 -2.272056 -4.249596 4.165667
## X620 3.072693 -2.449115 -4.629668 4.572474
## X621 2.793616 -2.565900 -4.442201 4.376271
## X622 2.903617 -2.493625 -4.781907 4.220791
## X623 2.928524 -2.164564 -3.519643 4.451081
## X624 3.091951 -2.401743 -4.575611 4.980549
## X626 2.921547 -2.250942 -3.769656 4.746189
## X627 3.072230 -2.174192 -3.556098 4.917397
## X629 2.467252 -2.327698 -4.551629 3.639212
## X630 2.700018 -2.176834 -4.510770 3.857866
## X631 3.043570 -2.085057 -3.453965 4.668773
## X633 2.629007 -2.561226 -3.234497 4.031624
## X634 3.171365 -2.187472 -3.631366 5.090232
## X635 3.175551 -2.143873 -3.767923 5.085404
## X636 3.044999 -2.259526 -4.042701 4.972347
## X637 2.946542 -2.508503 -4.686814 4.428254
## X638 2.852439 -2.238672 -2.452711 4.332192
## X639 2.802754 -2.319630 -5.182848 4.080869
## X640 3.059176 -2.406946 -4.103184 4.635650
## X641 2.683758 -2.324524 -2.367871 3.669295
## X642 3.199489 -2.233992 -2.879551 5.111247
## X643 2.759377 -2.295609 -3.880040 4.179793
## X644 2.804572 -2.389015 -4.006883 4.377888
## X645 2.978077 -2.389451 -3.815350 4.484527
## X647 2.781920 -2.239610 -2.840611 4.001364
## X648 3.176803 -2.051048 -2.683114 4.982438
## X650 3.043093 -2.205458 -4.071019 5.009980
## X651 2.763800 -2.227478 -3.331205 4.376271
## X652 3.215269 -2.241490 -2.865933 5.099260
## X653 3.269189 -2.107841 -2.805112 5.044600
## X654 2.750471 -2.330676 -4.010739 4.510643
## X655 2.918851 -2.315265 -4.105001 4.714115
## X656 3.066191 -2.359791 -3.512241 4.821893
## X658 3.081910 -2.433605 -3.691683 4.904441
## X659 2.723924 -2.178599 -3.120842 3.936655
## X660 3.178887 -2.410839 -4.007433 4.812472
## X661 3.125005 -2.385967 -3.711534 4.581390
## X663 2.906901 -2.280824 -4.205723 4.588794
## X664 2.987196 -2.264326 -3.292792 4.458901
## X666 2.881443 -2.258568 -4.440504 4.185067
## X667 2.992728 -2.278869 -4.224681 4.614531
## X668 2.552565 -2.409836 -4.319991 3.828226
## X669 2.984166 -2.327698 -3.542185 4.927712
## X670 3.218076 -2.355142 -4.207737 5.196499
## X671 2.597491 -2.145581 -4.524512 4.060557
## X672 3.021400 -2.524105 -5.099794 5.051957
## X673 2.965273 -2.297598 -3.818533 4.653708
## X674 2.959587 -2.303686 -3.808114 4.385955
## X675 2.744704 -1.967542 -3.536330 4.311499
## X676 2.908539 -2.169804 -3.767923 4.822564
## X678 2.979095 -2.020418 -2.445532 4.737167
## X680 2.832625 -2.266253 -3.529485 4.232863
## X681 3.033028 -2.309308 -3.208431 4.553792
## X682 2.978077 -2.546314 -2.597493 4.419537
## X683 3.005187 -2.187472 -3.284215 4.340417
## X684 2.761907 -2.162823 -3.810821 4.067964
## X686 2.757475 -2.357886 -2.694147 3.818947
## X688 3.131573 -2.158485 -3.224894 4.904441
## X690 2.381396 -2.367337 -4.180556 3.702239
## X691 2.840247 -2.249993 -3.862757 4.510643
## X692 3.005683 -1.933093 -2.322176 4.440088
## X693 2.387845 -2.206366 -4.361440 3.703328
## X694 2.796671 -2.642965 -3.420380 4.340417
## X696 3.206398 -2.379682 -4.444753 5.217803
## X697 2.939691 -2.498965 -3.600502 4.573218
## X698 2.796671 -2.162823 -3.173663 3.945456
## X699 3.223664 -2.287696 -3.448604 5.096856
## X700 2.587012 -2.238672 -3.706636 3.894116
## X702 3.069912 -2.294617 -3.936316 4.979920
## X703 2.634045 -2.357886 -4.189755 4.033502
## X705 3.112181 -2.401853 -4.421183 5.084195
## X707 2.733718 -2.339353 -4.185802 4.279690
## X708 2.866193 -2.148149 -3.358138 4.229421
## X710 2.482404 -2.380547 -5.175038 3.488165
## X711 2.893146 -2.330882 -3.943514 4.538741
## X712 2.851284 -2.214574 -4.054163 4.648665
## X713 2.767576 -2.444494 -4.158563 4.262772
## X714 2.706048 -2.551944 -4.027995 4.167437
## X716 2.808197 -2.215490 -3.315111 4.621105
## X717 2.932260 -2.508626 -3.020640 4.553792
## X718 2.719979 -2.305590 -3.773566 4.089126
## X719 2.885359 -2.532753 -4.140179 4.316482
## X720 3.033991 -2.175952 -4.472389 4.449513
## X721 3.030134 -2.363929 -2.915813 4.853256
## X722 2.730464 -2.233059 -2.344866 4.055916
## X723 2.571084 -2.327493 -4.712199 3.737909
## X724 2.730464 -2.366164 -3.903559 4.147887
## X725 2.887033 -2.447149 -4.159203 4.534963
## X727 2.968361 -2.597628 -3.626468 4.741335
## X728 2.544747 -2.373974 -4.626496 3.953251
## X729 2.561868 -2.588269 -5.093908 3.932732
## X730 3.004692 -2.217325 -3.727620 4.608673
## X731 2.768832 -2.442537 -3.488391 3.894116
## X732 2.898671 -2.189256 -3.782311 4.621834
## X733 3.100993 -2.290657 -3.449863 4.783310
## X735 2.983660 -2.474442 -4.830441 4.579906
## X736 2.273156 -2.344032 -4.623742 3.221497
## X737 2.933857 -2.423059 -3.700952 4.615263
## X738 3.205993 -2.254748 -3.314836 5.020540
## X739 2.830268 -2.317191 -4.290359 4.360856
## X740 2.516890 -2.274970 -4.439656 3.665973
## X741 2.977059 -2.402626 -4.544075 4.863182
## X742 2.475698 -2.073857 -3.763172 3.815845
## X743 2.688528 -2.296603 -3.853283 3.792962
## X744 2.718001 -2.431328 -4.588313 4.028805
## X745 2.670694 -2.392729 -4.827439 3.815845
## X746 2.893700 -2.333147 -2.429510 4.471360
## X748 3.100993 -2.772429 -6.095937 4.806397
## X749 2.569554 -2.437374 -5.057098 3.721768
## X750 3.085116 -2.212744 -3.674188 5.052569
## X751 3.279783 -2.170680 -3.045133 5.090835
## X753 2.702703 -2.401411 -3.289298 3.883102
## X754 3.109507 -2.401632 -4.272276 4.738557
## X755 2.715357 -2.378710 -4.422849 4.207786
## X756 2.922086 -2.454804 -4.690619 4.619646
## X757 2.844328 -2.325444 -4.743973 4.225973
## X758 2.855895 -2.295609 -4.735735 4.628388
## X759 2.766319 -2.515778 -3.811273 4.065190
## X760 3.140698 -2.230264 -1.999522 5.304618
## X761 3.063858 -2.436231 -4.022955 4.401206
## X762 2.902520 -2.666429 -5.000289 4.177151
## X764 3.144583 -2.259526 -2.971625 4.721123
## X766 3.104138 -2.120264 -3.396807 5.122583
## X767 3.083743 -2.607617 -2.938218 4.495315
## X768 3.113071 -2.462402 -3.297378 5.003747
## X769 3.006672 -2.315468 -4.137043 4.879637
## X770 2.973487 -2.344866 -4.029119 4.761381
## X771 2.961141 -2.411508 -3.661653 4.581390
## X772 3.283539 -2.170680 -2.971820 5.042143
## X773 3.167583 -2.022683 -3.471191 5.551376
## X774 2.923162 -2.306091 -3.957544 4.490698
## X776 2.848971 -2.217325 -4.382827 4.378696
## X777 3.008648 -2.433605 -4.197707 4.522074
## X778 3.115292 -2.300587 -3.492984 4.815841
## X779 2.558002 -2.503234 -4.393290 3.696783
## X781 2.941276 -2.422383 -4.058784 4.517508
## X782 2.916148 -2.169804 -3.585601 3.959079
## X783 3.241029 -2.296603 -2.458654 4.741335
## X784 3.170106 -2.357781 -3.294138 5.172099
## X785 2.829087 -2.276917 -3.370280 4.660893
## X787 2.861057 -2.519001 -3.467337 4.477566
## X788 3.070840 -2.366271 -3.451754 4.780580
## X789 3.480317 -2.474560 -3.632877 5.725074
## X790 2.577182 -2.338627 -4.077487 3.743263
## X791 2.631889 -2.252843 -3.766193 3.825137
## X793 3.008155 -2.277892 -3.740594 4.890764
## X794 2.834389 -2.471596 -4.506230 4.409194
## X795 2.600465 -2.312030 -4.248895 3.801311
## X797 2.741485 -2.480397 -3.439834 4.039126
## X798 3.176803 -2.537928 -3.774873 4.956498
## X799 3.105931 -2.218244 -3.255021 4.881604
## X800 2.948641 -2.170680 -3.908031 4.509879
## X801 3.298795 -2.676116 -4.106215 5.107058
## X802 3.520757 -2.553614 -4.988923 5.547844
## X803 3.325396 -2.390433 -3.881494 5.315680
## X804 2.766948 -2.469348 -4.565949 4.025039
## X805 3.056357 -2.400198 -4.280915 4.890111
## X806 3.294725 -2.352931 -3.553300 5.152173
## X807 3.066191 -2.482310 -3.463179 4.605738
## X808 3.326833 -2.498235 -3.559607 5.484477
## X810 2.747271 -2.362017 -4.383628 4.014653
## X811 2.710713 -2.535022 -5.312416 4.136255
## X812 2.900872 -2.344241 -2.827848 4.733688
## X814 3.157000 -2.275943 -3.425900 4.906389
## X815 2.988708 -2.234926 -4.278748 4.835955
## X817 2.646884 -2.434974 -2.883833 3.883102
## X819 2.703373 -2.289669 -4.399783 4.208655
## X821 2.915064 -2.370329 -4.542195 4.316482
## X822 2.986692 -2.242431 -2.984397 4.562778
## X824 2.961658 -2.268184 -4.017384 4.485299
## X825 2.836150 -2.210918 -3.619727 4.283900
## X826 3.359333 -2.379466 -3.043873 5.253674
## X827 2.848971 -2.013654 -3.081726 4.333015
## X828 3.144152 -2.199126 -2.814244 4.977398
## X829 3.513335 -2.241490 -3.666727 5.913428
## X830 3.298057 -2.302585 -4.149012 5.412105
## X831 3.138100 -2.446225 -4.504420 4.966653
## X832 3.096934 -2.408057 -2.816582 4.683033
## X833 2.964242 -2.545931 -4.698932 4.979289
## X834 3.094219 -2.330367 -4.417861 4.827259
## X835 3.437851 -2.357147 -4.214480 5.806493
## X836 2.941804 -2.334282 -3.329528 4.355967
## X837 3.083743 -2.531244 -3.727205 4.874383
## X838 2.785628 -2.361804 -3.826763 4.412381
## X839 3.015045 -2.223774 -3.039684 4.534207
## X840 2.822569 -2.744351 -5.596723 4.161235
## X841 2.568022 -2.319324 -4.490057 3.725005
## X842 3.044046 -2.364354 -3.003764 4.748958
## X843 2.751748 -2.403843 -4.540319 4.181552
## X844 3.197856 -2.424188 -4.449022 5.162741
## X845 2.854169 -2.099644 -4.207065 4.008967
## X846 2.650421 -2.366697 -4.710753 4.008967
## X847 2.994732 -2.416538 -4.497213 4.464360
## X848 2.881443 -2.532250 -5.244966 4.600594
## X849 2.719979 -2.352196 -4.172739 4.255969
## X851 2.640485 -2.549381 -4.239139 3.938613
## X852 2.900322 -2.266253 -3.817167 4.781263
## X853 2.932260 -2.238672 -3.357851 4.525113
## X855 2.912351 -2.477772 -4.827314 4.367359
## X856 3.033028 -2.452827 -2.965009 4.686585
## X857 2.574138 -2.665709 -4.308776 3.654863
## X858 2.993730 -2.523232 -2.493503 4.305672
## X859 2.938633 -2.440354 -4.272276 4.603535
## X860 2.982140 -2.435317 -2.240550 4.288943
## X861 2.949688 -2.408835 -3.856115 4.607206
## X862 2.773838 -2.297598 -3.910524 4.096440
## X863 2.859913 -2.480277 -4.199705 4.574706
## X864 2.623218 -2.336452 -4.439656 3.860909
## X866 2.513656 -2.462989 -4.405500 3.573135
## X868 2.899772 -2.721744 -4.285263 4.537986
## X869 3.139400 -2.287696 -4.238446 4.458120
## X871 2.990217 -2.470885 -3.390554 4.366547
## X872 3.172203 -2.225624 -3.050822 4.833951
## X873 2.923699 -2.236797 -4.671096 4.482982
## X874 2.899221 -2.424414 -3.629856 4.244873
## X875 3.198265 -2.593740 -3.756302 4.976136
## X877 2.667228 -2.658546 -5.321995 4.109184
## X878 2.542389 -2.606939 -5.587067 3.805473
## X879 2.627563 -2.482669 -5.357855 3.852784
## X880 2.950212 -2.428829 -4.698383 4.633474
## X881 2.753024 -2.574656 -5.112502 4.256821
## X883 2.372111 -2.453757 -4.312501 3.334618
## X884 2.923162 -2.231195 -4.266557 4.314822
## X885 2.824351 -2.463811 -5.805151 4.076268
## X887 2.937573 -2.328313 -4.098955 4.518270
## X888 2.939162 -2.305790 -2.719617 4.393191
## X889 2.833213 -2.582696 -4.529135 4.121857
## X890 2.783776 -2.243373 -3.154728 4.157683
## X891 2.978586 -2.523232 -4.285989 4.363297
## X892 2.589267 -2.176834 -3.904055 4.199074
## X893 3.068518 -2.145581 -3.716867 4.991235
## X894 2.721953 -2.444955 -4.255923 4.225110
## X897 2.886475 -2.566160 -4.978120 4.298995
## X898 3.030617 -2.146436 -3.871361 4.896635
## X899 3.085573 -2.149864 -3.281816 4.534207
## X900 2.741485 -2.354826 -3.428055 4.276316
## X901 2.962692 -2.345597 -3.424978 4.273783
## X902 2.988708 -2.249993 -4.506230 4.576936
## X903 2.693275 -2.488192 -4.944286 4.283059
## X904 2.945491 -2.487350 -4.775721 4.768255
## X907 3.064792 -2.395139 -3.244963 5.143922
## X908 2.863914 -2.295609 -4.234297 4.654427
## X909 3.189241 -2.235861 -3.926629 4.919334
## X910 2.805782 -2.327800 -3.489045 4.236301
## X911 2.823757 -2.467342 -3.360727 4.366547
## X912 2.705380 -2.270118 -3.975495 4.093700
## X914 2.737609 -2.162823 -4.512591 3.928802
## X915 2.688528 -2.314455 -3.063797 4.054986
## X916 2.939162 -2.478607 -4.513503 4.670202
## X918 2.774462 -2.399537 -4.762058 4.173623
## X920 2.837323 -2.582167 -4.963132 4.079030
## X921 2.955951 -2.085057 -2.724332 4.454212
## X924 2.644045 -2.620864 -3.385226 3.685830
## X926 2.922624 -2.223774 -3.236022 4.506820
## X927 2.785628 -2.436917 -4.835968 4.562030
## X929 2.907993 -2.293625 -4.712533 4.519792
## X930 2.894253 -2.598837 -5.361683 4.190330
## X931 3.071303 -2.455503 -3.889772 4.818532
## X933 2.906354 -2.334489 -4.014610 4.552291
## X934 2.830268 -2.533635 -4.382027 4.252561
## X935 3.080992 -2.391416 -3.960163 4.620376
## X937 2.891482 -2.382603 -4.277306 4.444020
## X938 2.847812 -2.366164 -4.499010 4.625478
## X939 3.086487 -2.241490 -3.568079 4.578422
## X940 3.148024 -2.328724 -3.239844 4.938626
## X942 2.714695 -2.301586 -3.621221 4.264469
## X943 2.853593 -2.359579 -3.965951 4.374653
## X944 2.776954 -2.488674 -4.143325 4.121857
## X945 2.776954 -2.314658 -4.010739 4.023154
## X946 3.006672 -2.399867 -2.571380 4.346158
## X947 3.339677 -2.588003 -4.420352 5.217230
## X948 2.718001 -2.492778 -3.511906 4.069812
## X949 2.935451 -2.107018 -3.090263 5.050733
## X951 2.703373 -2.527355 -3.961739 4.177151
## X952 3.123246 -2.668589 -3.087848 4.785356
## X954 2.618855 -2.481353 -3.876173 3.849729
## X955 3.148024 -2.443918 -4.050136 4.981809
## X957 2.782539 -2.655553 -4.258041 4.100089
## X958 2.740840 -2.481114 -2.707700 4.003267
## X959 3.144583 -2.292635 -3.194915 4.900541
## X960 2.503074 -2.302985 -4.294016 3.667081
## X961 2.823757 -2.264326 -3.929169 4.344519
## X962 2.994231 -2.154165 -3.530851 4.832614
## X963 3.103689 -2.148149 -3.289835 4.787400
## X964 2.874694 -2.273998 -4.115977 4.578422
## X965 2.843746 -2.520119 -4.363794 4.544020
## X966 2.938633 -2.245260 -3.933757 4.680188
## X968 2.696652 -2.558639 -4.220588 4.134460
## X969 2.848392 -2.398325 -3.930187 4.479114
## X970 3.045474 -2.095571 -3.291984 4.721123
## X971 2.389680 -2.422270 -4.419521 4.115529
## X972 2.906354 -2.610334 -3.293330 4.488386
## X975 2.922624 -2.298593 -3.847172 4.562030
## X977 3.061988 -2.583490 -3.105547 4.666626
## X978 3.028199 -2.267218 -3.585601 4.549287
## X979 2.885917 -2.443573 -3.767923 4.796917
## X981 2.823163 -2.228406 -4.671844 4.625478
## X982 3.076390 -2.529611 -3.621595 4.735776
## X983 3.096030 -2.463341 -3.572698 4.971715
## X984 3.394844 -2.486508 -4.249596 5.289608
## X986 3.077312 -2.259526 -4.089954 4.952042
## X988 2.498974 -2.432124 -4.371680 3.803393
## X989 3.063858 -2.284745 -4.662587 4.799630
## X990 2.946542 -2.459707 -3.888795 4.664478
## X991 2.637628 -2.272056 -2.948086 3.946432
## X992 2.773838 -2.218244 -3.909526 4.072581
## X994 2.950735 -2.230264 -3.971242 4.374653
## X995 3.057768 -2.511210 -5.167816 4.800985
## X996 2.706716 -2.321156 -3.252691 4.241448
## X997 3.090133 -2.430305 -3.840633 5.002499
## X998 2.810607 -2.507030 -5.073096 4.129067
## X999 2.871868 -2.538814 -4.444753 4.188577
## X1000 3.114848 -2.307899 -2.778526 4.706382
## X1001 2.872434 -2.249993 -3.412764 4.353519
## X1002 2.972464 -2.177716 -3.606378 4.523594
## X1003 3.089678 -2.284745 -3.197114 4.932212
## X1004 2.829678 -2.416426 -4.095345 4.151454
## X1005 2.976549 -2.244316 -4.022396 4.923849
## X1006 2.972464 -2.392948 -4.296216 4.470584
## X1007 2.771338 -2.470057 -4.346659 4.242305
## X1008 2.975530 -2.443688 -4.514416 4.737167
## X1009 2.751110 -2.529988 -4.684430 4.039126
## X1011 3.235536 -2.485187 -3.144696 5.207462
## X1012 2.759377 -2.428489 -4.460204 3.862936
## X1013 2.907993 -2.508134 -4.319240 4.385955
## X1014 2.824351 -2.413852 -4.160484 4.279690
## X1016 3.333275 -2.302885 -3.904551 5.378924
## X1017 2.871302 -2.388252 -4.447312 4.339596
## X1018 2.962175 -2.478368 -3.888306 4.763445
## X1019 3.021400 -2.334695 -3.875209 5.004371
## X1020 3.069912 -2.716133 -2.802965 4.748958
## X1021 3.218876 -2.271086 -3.948168 4.934138
## X1022 3.340385 -2.472543 -3.653898 5.343130
## X1024 2.841998 -2.455387 -4.763111 4.290621
## X1026 3.377246 -2.369045 -3.722229 5.393426
## X1027 3.228826 -2.431442 -4.818116 5.135645
## X1028 3.224062 -2.480636 -4.713424 4.992489
## X1029 3.339322 -2.527731 -4.366153 5.290165
## X1031 3.268428 -2.221927 -2.923598 4.960311
## X1032 3.295466 -2.659975 -4.086972 4.998750
## X1033 2.910174 -2.464163 -4.073954 4.442448
## X1035 3.002211 -2.490844 -2.709501 4.556042
## X1036 3.032064 -2.444725 -3.448604 4.553042
## X1037 2.895912 -2.487590 -4.376442 4.489157
## X1038 3.149740 -2.376339 -2.655695 4.796239
## X1039 2.900322 -2.141317 -3.440146 4.548535
## X1040 2.917230 -2.413964 -3.917538 4.565019
## X1041 3.337192 -2.435888 -4.163695 5.081777
## X1042 2.703373 -2.513553 -4.034191 3.934694
## X1044 2.748552 -2.295609 -3.476029 4.042868
## X1048 2.680336 -2.257612 -3.856588 4.269554
## X1049 2.970927 -2.276917 -2.905892 4.364923
## X1050 2.892037 -2.398325 -4.094745 4.727414
## X1051 2.956991 -2.526854 -4.526359 4.624021
## X1053 2.870169 -2.307598 -4.805330 4.403605
## X1054 2.423031 -2.260484 -4.006334 3.500171
## X1055 2.797891 -2.352406 -2.594141 4.194706
## X1056 2.824351 -2.448652 -4.273710 4.551541
## X1057 2.934920 -2.217325 -3.563834 4.897287
## X1058 2.783158 -2.182139 -4.348979 4.243161
## X1059 3.005683 -2.590667 -4.236369 4.634199
## X1062 3.023347 -2.301586 -3.611918 4.597650
## X1063 2.551786 -2.607481 -4.602175 3.744333
## X1064 3.022374 -2.612513 -3.886355 4.768255
## X1065 3.006178 -2.344762 -4.289630 4.769627
## X1067 2.851284 -2.415978 -4.289630 4.467474
## X1069 3.055886 -2.221005 -3.578770 4.921270
## X1070 2.817801 -2.314354 -4.006883 4.141631
## X1073 2.987196 -2.370650 -3.394420 4.430625
## X1074 2.554899 -1.811554 -3.091803 3.746469
## X1075 2.575661 -2.075450 -3.018387 3.916970
## X1076 2.997730 -2.210918 -3.817622 4.454212
## X1078 2.753661 -2.361592 -4.420352 3.889116
## X1079 3.175968 -2.134532 -3.007805 5.257064
## X1080 2.687167 -2.513430 -3.240099 3.873042
## X1081 2.687847 -2.468404 -4.425352 3.871024
## X1082 3.021400 -2.201835 -3.787595 4.849274
## X1083 2.614472 -2.319528 -4.010739 3.836443
## X1084 2.948116 -2.384338 -4.116590 4.740641
## X1085 2.923699 -2.254748 -4.109864 4.363297
## X1086 3.024320 -2.236797 -3.975495 4.607940
## X1087 3.008648 -2.266253 -3.770958 4.535719
## X1088 2.902520 -2.105375 -3.759731 4.469807
## X1089 2.815409 -2.184802 -4.159844 4.255969
## X1091 3.072693 -2.273026 -3.438276 4.660893
## X1092 2.987196 -2.463811 -5.132803 4.624750
## X1093 2.927453 -2.311021 -3.793796 4.565765
## X1095 2.572612 -2.267218 -4.038721 3.957138
## X1096 2.931194 -2.230264 -4.238446 4.530422
## X1099 2.598235 -2.207275 -4.462803 3.680332
## X1100 2.865624 -2.232127 -4.009085 4.735080
## X1101 2.996732 -2.286712 -4.058784 4.792163
## X1102 2.793004 -2.377632 -4.929793 4.120954
## X1103 3.028683 -2.390761 -3.477323 4.676626
## X1104 2.869035 -2.334385 -3.387886 4.630569
## X1107 3.196221 -2.090705 -3.353837 5.011847
## X1108 3.238286 -2.513553 -4.636454 4.931570
## X1110 2.670002 -2.304186 -3.191261 4.073504
## X1111 3.218476 -2.426223 -3.067658 4.983068
## X1112 3.235536 -2.491931 -4.446458 5.018060
## X1114 3.030134 -2.345701 -3.829522 4.499157
## X1115 3.145445 -2.380979 -3.863709 4.811124
## X1116 2.794228 -2.360850 -4.927168 4.258523
## X1117 2.808197 -2.421707 -3.478943 4.281375
## X1118 2.962175 -2.466163 -4.489167 4.562778
## X1119 3.186766 -2.502012 -3.979232 4.965386
## X1120 3.067122 -2.599510 -4.506230 4.500691
## X1121 3.110845 -2.346955 -3.489701 4.754487
## X1122 3.382015 -2.491810 -4.395720 5.238363
## X1123 3.088311 -2.381628 -3.998671 4.522074
## X1124 3.364533 -2.510471 -3.838308 5.223531
## X1125 3.318178 -2.404618 -3.598673 5.175599
## X1126 2.975019 -2.299590 -3.806762 4.351068
## X1127 3.327910 -2.510471 -4.488276 5.136237
## X1128 3.121483 -2.468286 -3.070671 4.685165
## X1130 3.301377 -2.309710 -3.620100 5.072078
## X1131 3.379974 -2.597090 -4.724179 5.365966
## X1132 3.421653 -2.255702 -3.027429 5.598355
## X1133 3.222469 -2.208184 -3.144232 4.832614
## X1134 3.108614 -2.198225 -3.543568 4.622564
## X1135 3.341093 -2.324831 -3.720164 5.363258
## X1137 3.378611 -2.138767 -2.787418 5.425895
## smoothness_worst symmetry_worst
## X1 -1.401837 -0.9485186
## X2 -1.552206 -1.8138504
## X3 -1.468032 -1.3273311
## X4 -1.246824 -0.4547732
## X5 -1.495633 -2.1134503
## X6 -1.343543 -1.1682237
## X7 -1.468808 -1.6137366
## X9 -1.373392 -1.0226796
## X10 -1.323124 -1.0268307
## X11 -1.577215 -1.6835473
## X12 -1.486854 -1.2478490
## X14 -1.599859 -1.7735849
## X15 -1.391541 -1.3351867
## X17 -1.460319 -1.6339618
## X18 -1.344209 -1.2853171
## X20 -1.469584 -1.6655621
## X21 -1.520913 -1.5444060
## X22 -1.515956 -2.0406102
## X23 -1.489239 -0.9275957
## X25 -1.338889 -1.3273311
## X26 -1.429814 -1.1365073
## X27 -1.437240 -1.0628195
## X28 -1.510212 -2.1336080
## X29 -1.395077 -1.1516587
## X30 -1.544904 -1.8096966
## X32 -1.396495 -0.8985507
## X33 -1.397561 -1.3662211
## X34 -1.443230 -1.3004918
## X35 -1.467258 -1.0606668
## X36 -1.423188 -0.8679915
## X38 -1.677854 -2.4867416
## X39 -1.694115 -3.0556014
## X40 -1.406135 -1.7749290
## X41 -1.617070 -1.6551407
## X43 -1.548331 -0.9266552
## X45 -1.445488 -1.2910944
## X47 -1.527155 -1.5892127
## X49 -1.448886 -1.8159323
## X50 -1.585739 -1.7326167
## X51 -1.621318 -2.0547020
## X52 -1.619427 -2.1292007
## X53 -1.593905 -1.7898096
## X55 -1.489637 -1.8669460
## X56 -1.547473 -1.4783924
## X57 -1.401122 -1.3628885
## X58 -1.498044 -1.2888687
## X59 -1.652261 -2.0497116
## X60 -1.363097 -1.5245369
## X62 -1.395786 -1.6686442
## X63 -1.395431 -1.7502930
## X64 -1.670976 -1.4910873
## X66 -1.392600 -1.4705280
## X67 -1.428706 -1.7280747
## X68 -1.530085 -2.0824829
## X73 -1.415161 -1.4747157
## X74 -1.480924 -1.9306463
## X75 -1.579449 -1.9088155
## X76 -1.446619 -1.8851434
## X77 -1.465324 -1.8418938
## X78 -1.454964 -1.2655509
## X79 -1.395786 -0.7116307
## X80 -1.530504 -1.7938986
## X81 -1.425391 -1.8055564
## X83 -1.419530 -2.1213029
## X84 -1.488443 -2.1603515
## X85 -1.494430 -1.4406136
## X86 -1.486061 -1.2902036
## X87 -1.523404 -1.6393726
## X88 -1.547473 -1.1798150
## X90 -1.535556 -1.5629177
## X91 -1.607252 -1.9825153
## X92 -1.544049 -1.9559389
## X94 -1.509803 -1.8647794
## X95 -1.427599 -1.7569038
## X96 -1.573211 -1.2928782
## X97 -1.595732 -2.2380872
## X99 -1.473086 -1.7986859
## X100 -1.473086 -1.8362356
## X101 -1.540640 -1.8844106
## X102 -1.415525 -1.6935843
## X105 -1.560451 -1.7622177
## X106 -1.320199 -1.5651813
## X108 -1.575878 -1.6618738
## X110 -1.374774 -1.7602223
## X111 -1.459169 -1.9738585
## X112 -1.531344 -2.2390390
## X113 -1.717446 -2.0841850
## X114 -1.525902 -2.0970190
## X115 -1.366172 -1.6973693
## X117 -1.578108 -2.9206783
## X118 -1.315025 -1.3402996
## X119 -1.322473 -1.4953499
## X120 -1.627498 -0.8624052
## X121 -1.428706 -1.6417852
## X122 -1.445488 -1.7177547
## X124 -1.520499 -1.7209705
## X126 -1.587999 -2.1134503
## X127 -1.457637 -1.3951992
## X128 -1.652261 -1.7522726
## X131 -1.474648 -1.3956885
## X132 -1.429444 -1.7549169
## X133 -1.487251 -1.3903175
## X134 -1.558708 -1.8327119
## X135 -1.459935 -1.5869030
## X137 -1.538094 -2.8336824
## X138 -1.573211 -1.8662234
## X140 -1.498446 -2.3622810
## X141 -1.553935 -1.5892127
## X142 -1.520085 -1.7850558
## X143 -1.480137 -1.9344474
## X144 -1.520913 -1.3571983
## X145 -1.625591 -2.1702883
## X146 -1.475821 -1.8298999
## X148 -1.664214 -1.7450294
## X151 -1.527155 -1.5377457
## X152 -1.398983 -1.4700056
## X153 -1.529246 -1.5874800
## X154 -1.508987 -1.7404419
## X155 -1.454964 -1.2237105
## X156 -1.561324 -1.5845978
## X157 -1.478172 -2.0299327
## X158 -1.726991 -1.9785734
## X159 -1.536401 -1.9888468
## X160 -1.581689 -1.8221988
## X161 -1.502079 -1.5533453
## X163 -1.470361 -1.3136025
## X164 -1.464938 -2.1996053
## X165 -1.556535 -1.3384376
## X166 -1.640502 -1.8880790
## X168 -1.583037 -1.7729134
## X169 -1.494831 -2.3033148
## X170 -1.561761 -2.0790851
## X171 -1.491231 -1.7615522
## X172 -1.484873 -1.7241946
## X173 -1.435005 -1.5267281
## X174 -1.561761 -2.5859017
## X175 -1.625591 -1.8418938
## X176 -1.585739 -1.9283710
## X177 -1.525485 -1.9118051
## X178 -1.479351 -1.6190575
## X179 -1.763600 -2.1748286
## X180 -1.585739 -2.7364649
## X181 -1.457254 -1.7424059
## X182 -1.450022 -1.1242373
## X183 -1.479744 -1.4114596
## X184 -1.604471 -2.6997069
## X186 -1.438733 -1.6929546
## X187 -1.553935 -1.5322240
## X188 -1.516368 -1.9436150
## X189 -1.502888 -1.5355339
## X190 -1.616129 -2.0144782
## X192 -1.694063 -2.2845122
## X193 -1.824755 -2.5774861
## X195 -1.519257 -1.6514837
## X196 -1.615659 -1.6369648
## X198 -1.724360 -2.1091071
## X199 -1.516780 -1.5394073
## X202 -1.492829 -1.6961064
## X203 -1.433147 -1.5366393
## X204 -1.209422 -1.0042088
## X205 -1.475039 -1.6429933
## X206 -1.450022 -1.4224305
## X207 -1.479351 -1.6581966
## X208 -1.609112 -1.4948162
## X210 -1.559143 -2.1522740
## X212 -1.534290 -1.9722906
## X214 -1.550051 -2.9953191
## X215 -1.424656 -0.9098798
## X216 -1.461856 -1.3195307
## X218 -1.686819 -1.7345684
## X219 -1.504104 -1.6096144
## X220 -1.482107 -1.8397690
## X224 -1.427231 -1.1650483
## X225 -1.535978 -1.9952086
## X226 -1.527155 -1.6143267
## X227 -1.508987 -1.9110570
## X229 -1.556969 -1.7622177
## X230 -1.348222 -1.4264463
## X231 -1.373392 -1.5869030
## X232 -1.703821 -1.7470007
## X233 -1.663010 -1.7068831
## X234 -1.558708 -2.0513730
## X235 -1.445111 -1.8090056
## X236 -1.531344 -2.2390390
## X238 -1.556535 -2.2276590
## X240 -1.499252 -1.7443730
## X241 -1.535133 -1.9762139
## X243 -1.506542 -1.4773407
## X244 -1.695111 -1.8756488
## X245 -1.460703 -1.7011661
## X247 -1.613783 -1.8021165
## X248 -1.545331 -1.8932321
## X249 -1.446997 -1.4254410
## X250 -1.489637 -1.8749213
## X251 -1.563950 -1.5771365
## X252 -1.576324 -1.8208036
## X253 -1.370978 -1.8145440
## X254 -1.478958 -1.5702903
## X255 -1.447752 -1.4406136
## X257 -1.533868 -1.7675541
## X258 -1.352253 -1.5039222
## X260 -1.313415 -1.3748365
## X261 -1.438360 -1.5629177
## X264 -1.621791 -1.8611765
## X265 -1.425023 -1.5267281
## X267 -1.563074 -1.6885556
## X268 -1.660208 -2.0439127
## X269 -1.544476 -1.3314830
## X270 -1.511439 -1.9185567
## X271 -1.738456 -2.0340294
## X273 -1.537670 -1.7575668
## X276 -1.501675 -2.2447636
## X277 -1.543196 -1.8083150
## X278 -1.550051 -1.9474538
## X279 -1.638076 -2.1389154
## X280 -1.575433 -1.6791818
## X281 -1.346547 -1.5039222
## X282 -1.644889 -1.5915268
## X283 -1.439855 -1.3379726
## X284 -1.499252 -1.8000570
## X286 -1.643912 -1.9960060
## X287 -1.606788 -2.0282975
## X288 -1.682219 -2.1621529
## X289 -1.648811 -1.6791818
## X290 -1.605860 -1.4990925
## X291 -1.663562 -2.1959068
## X292 -1.520499 -1.6748318
## X293 -1.453060 -1.4401046
## X294 -1.497641 -1.5915268
## X295 -1.526737 -2.1091071
## X296 -1.582138 -1.7642162
## X297 -1.698055 -2.3203498
## X298 -1.597105 -2.4969375
## X299 -1.691083 -1.8954469
## X301 -1.448508 -1.6711155
## X302 -1.638076 -1.8597382
## X303 -1.506542 -1.4847225
## X304 -1.480137 -2.2514717
## X305 -1.593905 -2.2562827
## X306 -1.685886 -1.5114749
## X307 -1.601239 -1.8844106
## X309 -1.713449 -2.2005314
## X310 -1.654735 -2.3571020
## X311 -1.558708 -1.3869129
## X312 -1.657217 -1.9762139
## X313 -1.571438 -1.9497625
## X314 -1.618012 -1.4350269
## X315 -1.506542 -1.5680169
## X316 -1.612379 -2.5679247
## X317 -1.662208 -2.1766489
## X318 -1.471917 -1.7715714
## X319 -1.559579 -1.5719981
## X320 -1.747337 -2.5871077
## X321 -1.484477 -1.9163022
## X322 -1.636142 -1.6184651
## X323 -1.429075 -2.0978789
## X324 -1.412624 -0.6826914
## X325 -1.544476 -1.8771050
## X326 -1.491630 -1.8575837
## X327 -1.533868 -2.3643578
## X329 -1.413348 -1.5903692
## X330 -1.475039 -1.8235956
## X331 -1.471528 -1.6399753
## X332 -1.530924 -1.3351867
## X333 -1.475821 -1.4857809
## X334 -1.583937 -1.7695612
## X335 -1.559143 -1.9574875
## X336 -1.466097 -1.9050882
## X337 -1.594818 -2.0555355
## X338 -1.447374 -1.2973504
## X339 -1.484477 -1.7177547
## X340 -1.440979 -1.9276134
## X341 -1.528409 -1.6196501
## X342 -1.554367 -1.6624877
## X343 -1.478172 -1.4810257
## X344 -1.560888 -1.1446385
## X346 -1.501270 -2.0538690
## X347 -1.530504 -1.7267800
## X348 -1.560015 -1.5869030
## X350 -1.528827 -1.5782813
## X351 -1.664817 -1.8270941
## X352 -1.427968 -1.0696662
## X353 -1.435377 -1.2924320
## X354 -1.388371 -1.8822146
## X355 -1.722066 -1.9405520
## X357 -1.508171 -1.5845978
## X358 -1.589811 -2.1151915
## X359 -1.655231 -2.0538690
## X360 -1.512259 -2.0373158
## X361 -1.699057 -2.2323896
## X362 -1.595732 -1.8947083
## X364 -1.510212 -2.0875956
## X366 -1.514721 -1.9155516
## X367 -1.535133 -1.4969524
## X369 -1.500059 -1.9920239
## X370 -1.528409 -1.8201066
## X371 -1.479351 -0.8795614
## X372 -1.602161 -2.0104407
## X373 -1.572324 -1.8277950
## X374 -1.423555 -1.8568664
## X375 -1.611911 -1.4694835
## X376 -1.553502 -1.5617875
## X377 -1.594361 -1.9245875
## X378 -1.610510 -1.8532856
## X379 -1.536401 -1.4365479
## X380 -1.221525 -1.1031070
## X381 -1.406135 -1.4590896
## X384 -1.474648 -1.7668858
## X385 -1.583937 -1.8235956
## X386 -1.520913 -2.0185279
## X389 -1.613314 -2.3063064
## X390 -1.546616 -1.9405520
## X391 -1.508171 -1.6904389
## X392 -1.461856 -2.0447396
## X393 -1.381022 -1.5427374
## X394 -1.445865 -1.2325409
## X395 -1.472696 -1.6220237
## X396 -1.630368 -1.9817260
## X397 -1.474257 -1.8734676
## X398 -1.686456 -2.4856130
## X399 -1.604934 -1.9298875
## X400 -1.549621 -1.7938986
## X402 -1.495633 -2.0323892
## X404 -1.581241 -1.4831367
## X405 -1.639046 -2.1721026
## X406 -1.500059 -2.2154336
## X407 -1.566145 -1.7945814
## X408 -1.693330 -2.0096347
## X409 -1.453440 -1.6155076
## X410 -1.551343 -1.4025614
## X411 -1.464552 -1.6680271
## X412 -1.497641 -1.6527015
## X413 -1.620845 -2.1030497
## X414 -1.625115 -1.5561527
## X415 -1.592083 -1.5174436
## X416 -1.475039 -1.6066785
## X418 -1.440230 -1.6496593
## X419 -1.520085 -1.7966320
## X421 -1.547473 -1.6303680
## X422 -1.520913 -1.7615522
## X423 -1.479351 -1.7884496
## X424 -1.592538 -1.8180177
## X425 -1.486061 -1.5377457
## X426 -1.602161 -2.1265631
## X427 -1.514721 -1.6393726
## X429 -1.647829 -2.0970190
## X430 -1.629410 -2.2983428
## X431 -1.477780 -1.7358713
## X432 -1.465711 -1.9559389
## X433 -1.386615 -1.6321636
## X435 -1.560888 -1.9801488
## X436 -1.405058 -1.5471923
## X437 -1.562636 -1.4747157
## X438 -1.570996 -1.8313051
## X439 -1.605860 -1.9896404
## X440 -1.645868 -2.3274232
## X441 -1.488841 -1.9683790
## X443 -1.627498 -2.6386361
## X446 -1.521328 -1.9230772
## X447 -1.484873 -1.6686442
## X448 -1.557403 -1.3333333
## X449 -1.619427 -2.0234038
## X450 -1.498044 -2.1996053
## X451 -1.690457 -2.1657627
## X452 -1.436122 -2.1766489
## X453 -1.565266 -2.0430863
## X456 -1.581241 -2.2678960
## X457 -1.482896 -1.7241946
## X458 -1.530504 -2.0455670
## X459 -1.560888 -2.1648594
## X461 -1.409733 -1.6454131
## X464 -1.508579 -1.5300225
## X465 -1.533447 -2.2304954
## X466 -1.568346 -1.7496338
## X467 -1.504916 -1.9505330
## X468 -1.490035 -1.6172812
## X469 -1.565705 -2.1693820
## X470 -1.347217 -1.8778337
## X471 -1.553935 -1.5499851
## X473 -1.630847 -1.8575837
## X474 -1.692284 -2.0748497
## X475 -1.512668 -1.9367333
## X476 -1.515133 -1.6478377
## X477 -1.589811 -1.9730743
## X478 -1.694272 -1.8640580
## X479 -1.504510 -1.6879284
## X480 -1.494430 -1.4720966
## X481 -1.565266 -2.0773893
## X482 -1.631327 -2.1204282
## X483 -1.488046 -1.5207121
## X485 -1.431294 -1.9551652
## X486 -1.579897 -1.5185321
## X487 -1.594818 -2.0364934
## X488 -1.441353 -1.4996282
## X490 -1.658213 -0.9244642
## X491 -1.544476 -1.5921060
## X492 -1.669659 -2.7364649
## X495 -1.605860 -1.8583015
## X496 -1.561761 -2.1091071
## X499 -1.480531 -1.9920239
## X500 -1.460319 -2.0160966
## X503 -1.416979 -1.5606584
## X504 -1.569228 -1.7087947
## X505 -1.307322 -1.6285751
## X506 -1.274705 -1.7476584
## X508 -1.387668 -1.7932162
## X510 -1.363438 -1.6435978
## X511 -1.627020 -1.9193090
## X512 -1.596189 -2.1398020
## X513 -1.419165 -1.3402996
## X514 -1.558708 -1.9028570
## X515 -1.548761 -2.1867032
## X516 -1.453060 -1.6155076
## X517 -1.449644 -1.6072651
## X518 -1.478565 -1.9613670
## X519 -1.463396 -1.9359709
## X520 -1.456108 -1.6090266
## X521 -1.324101 -1.2964545
## X522 -1.508579 -1.5595304
## X523 -1.610510 -1.9551652
## X524 -1.475430 -1.7470007
## X528 -1.529246 -1.5863263
## X529 -1.487648 -2.3033148
## X530 -1.438733 -1.7925342
## X533 -1.541066 -1.7756016
## X535 -1.474257 -2.1802966
## X536 -1.539366 -1.6055062
## X538 -1.351243 -1.7776215
## X539 -1.544476 -1.6166897
## X540 -1.411177 -1.7864122
## X541 -1.507356 -2.1442434
## X542 -1.509395 -1.5427374
## X543 -1.633249 -1.8334159
## X545 -1.541066 -2.2173075
## X547 -1.532184 -1.8626165
## X549 -1.569228 -1.9590379
## X552 -1.620372 -1.5527846
## X553 -1.553935 -2.0765423
## X554 -1.612846 -2.0530365
## X555 -1.556969 -2.1065078
## X558 -1.627020 -2.0201513
## X559 -1.649795 -2.2088944
## X560 -1.526737 -2.3519414
## X561 -1.550912 -2.2163702
## X562 -1.700430 -3.0539870
## X563 -1.478565 -1.1276737
## X564 -1.482501 -1.6954754
## X565 -1.481318 -2.4065265
## X566 -1.583937 -1.9436150
## X567 -1.596189 -2.2466769
## X568 -1.391894 -1.1284389
## X569 -1.714905 -1.7326167
## X570 -1.401837 -0.9485186
## X571 -1.552206 -1.8138504
## X572 -1.468032 -1.3273311
## X573 -1.246824 -0.4547732
## X574 -1.495633 -2.1134503
## X576 -1.468808 -1.6137366
## X577 -1.390483 -1.5377457
## X579 -1.323124 -1.0268307
## X580 -1.577215 -1.6835473
## X582 -1.644401 -1.5488672
## X584 -1.391541 -1.3351867
## X585 -1.382068 -1.0794752
## X586 -1.460319 -1.6339618
## X587 -1.344209 -1.2853171
## X588 -1.442104 -1.8014296
## X591 -1.515956 -2.0406102
## X592 -1.489239 -0.9275957
## X593 -1.484873 -1.7648831
## X594 -1.338889 -1.3273311
## X595 -1.429814 -1.1365073
## X596 -1.437240 -1.0628195
## X597 -1.510212 -2.1336080
## X598 -1.395077 -1.1516587
## X599 -1.544904 -1.8096966
## X600 -1.450022 -1.4079909
## X602 -1.397561 -1.3662211
## X604 -1.467258 -1.0606668
## X605 -1.423188 -0.8679915
## X606 -1.467258 -1.3375078
## X607 -1.677854 -2.4867416
## X608 -1.694115 -3.0556014
## X609 -1.406135 -1.7749290
## X610 -1.617070 -1.6551407
## X612 -1.548331 -0.9266552
## X613 -1.435377 -1.2707870
## X614 -1.445488 -1.2910944
## X615 -1.381719 -1.2448554
## X616 -1.527155 -1.5892127
## X618 -1.448886 -1.8159323
## X620 -1.621318 -2.0547020
## X621 -1.619427 -2.1292007
## X622 -1.593905 -1.7898096
## X623 -1.534290 -1.6387702
## X624 -1.489637 -1.8669460
## X626 -1.401122 -1.3628885
## X627 -1.498044 -1.2888687
## X629 -1.363097 -1.5245369
## X630 -1.536401 -1.3534209
## X631 -1.395786 -1.6686442
## X633 -1.670976 -1.4910873
## X634 -1.323775 -1.4385789
## X635 -1.392600 -1.4705280
## X636 -1.428706 -1.7280747
## X637 -1.530085 -2.0824829
## X638 -1.453440 -1.0758313
## X639 -1.527572 -2.0970190
## X640 -1.571881 -1.9598138
## X641 -1.565705 -2.2126273
## X642 -1.415161 -1.4747157
## X643 -1.480924 -1.9306463
## X644 -1.579449 -1.9088155
## X645 -1.446619 -1.8851434
## X647 -1.454964 -1.2655509
## X648 -1.395786 -0.7116307
## X650 -1.425391 -1.8055564
## X651 -1.433147 -1.3676525
## X652 -1.419530 -2.1213029
## X653 -1.488443 -2.1603515
## X654 -1.494430 -1.4406136
## X655 -1.486061 -1.2902036
## X656 -1.523404 -1.6393726
## X658 -1.524236 -1.6686442
## X659 -1.535556 -1.5629177
## X660 -1.607252 -1.9825153
## X661 -1.544049 -1.9559389
## X663 -1.509803 -1.8647794
## X664 -1.427599 -1.7569038
## X666 -1.595732 -2.2380872
## X667 -1.519257 -2.5478042
## X668 -1.473086 -1.7986859
## X669 -1.473086 -1.8362356
## X670 -1.540640 -1.8844106
## X671 -1.415525 -1.6935843
## X672 -1.603546 -1.8532856
## X673 -1.424656 -1.9058328
## X674 -1.560451 -1.7622177
## X675 -1.320199 -1.5651813
## X676 -1.378587 -1.7756016
## X678 -1.374083 -1.1407587
## X680 -1.459169 -1.9738585
## X681 -1.531344 -2.2390390
## X682 -1.717446 -2.0841850
## X683 -1.525902 -2.0970190
## X684 -1.366172 -1.6973693
## X686 -1.578108 -2.9206783
## X688 -1.322473 -1.4953499
## X690 -1.428706 -1.6417852
## X691 -1.445488 -1.7177547
## X692 -1.375812 -1.5234428
## X693 -1.520499 -1.7209705
## X694 -1.650288 -2.4194174
## X696 -1.457637 -1.3951992
## X697 -1.652261 -1.7522726
## X698 -1.490832 -1.9298875
## X699 -1.536401 -1.4789186
## X700 -1.474648 -1.3956885
## X702 -1.487251 -1.3903175
## X703 -1.558708 -1.8327119
## X705 -1.477780 -1.7602223
## X707 -1.573211 -1.8662234
## X708 -1.480924 -1.4229317
## X710 -1.553935 -1.5892127
## X711 -1.520085 -1.7850558
## X712 -1.480137 -1.9344474
## X713 -1.520913 -1.3571983
## X714 -1.625591 -2.1702883
## X716 -1.491231 -0.6320347
## X717 -1.664214 -1.7450294
## X718 -1.519257 -1.8554329
## X719 -1.677342 -2.1256850
## X720 -1.527155 -1.5377457
## X721 -1.398983 -1.4700056
## X722 -1.529246 -1.5874800
## X723 -1.508987 -1.7404419
## X724 -1.454964 -1.2237105
## X725 -1.561324 -1.5845978
## X727 -1.726991 -1.9785734
## X728 -1.536401 -1.9888468
## X729 -1.581689 -1.8221988
## X730 -1.502079 -1.5533453
## X731 -1.603084 -2.0463949
## X732 -1.470361 -1.3136025
## X733 -1.464938 -2.1996053
## X735 -1.640502 -1.8880790
## X736 -1.471139 -2.3747864
## X737 -1.583037 -1.7729134
## X738 -1.494831 -2.3033148
## X739 -1.561761 -2.0790851
## X740 -1.491231 -1.7615522
## X741 -1.484873 -1.7241946
## X742 -1.435005 -1.5267281
## X743 -1.561761 -2.5859017
## X744 -1.625591 -1.8418938
## X745 -1.585739 -1.9283710
## X746 -1.525485 -1.9118051
## X748 -1.763600 -2.1748286
## X749 -1.585739 -2.7364649
## X750 -1.457254 -1.7424059
## X751 -1.450022 -1.1242373
## X753 -1.604471 -2.6997069
## X754 -1.525485 -1.5494260
## X755 -1.438733 -1.6929546
## X756 -1.553935 -1.5322240
## X757 -1.516368 -1.9436150
## X758 -1.502888 -1.5355339
## X759 -1.616129 -2.0144782
## X760 -1.434262 -0.7826129
## X761 -1.694063 -2.2845122
## X762 -1.824755 -2.5774861
## X764 -1.519257 -1.6514837
## X766 -1.361734 -1.6037499
## X767 -1.724360 -2.1091071
## X768 -1.516780 -1.5394073
## X769 -1.427231 -0.9009890
## X770 -1.473867 -1.8720155
## X771 -1.492829 -1.6961064
## X772 -1.433147 -1.5366393
## X773 -1.209422 -1.0042088
## X774 -1.475039 -1.6429933
## X776 -1.479351 -1.6581966
## X777 -1.609112 -1.4948162
## X778 -1.505728 -1.1128640
## X779 -1.559143 -2.1522740
## X781 -1.534290 -1.9722906
## X782 -1.594818 -2.9266464
## X783 -1.550051 -2.9953191
## X784 -1.424656 -0.9098798
## X785 -1.461856 -1.3195307
## X787 -1.686819 -1.7345684
## X788 -1.504104 -1.6096144
## X789 -1.482107 -1.8397690
## X790 -1.521328 -2.0996003
## X791 -1.494831 -1.6125574
## X793 -1.427231 -1.1650483
## X794 -1.535978 -1.9952086
## X795 -1.527155 -1.6143267
## X797 -1.597563 -1.6798045
## X798 -1.556969 -1.7622177
## X799 -1.348222 -1.4264463
## X800 -1.373392 -1.5869030
## X801 -1.703821 -1.7470007
## X802 -1.663010 -1.7068831
## X803 -1.558708 -2.0513730
## X804 -1.445111 -1.8090056
## X805 -1.531344 -2.2390390
## X806 -1.453821 -1.5903692
## X807 -1.556535 -2.2276590
## X808 -1.623214 -2.6004371
## X810 -1.535133 -1.9762139
## X811 -1.644401 -1.7132666
## X812 -1.506542 -1.4773407
## X814 -1.460703 -1.7011661
## X815 -1.440979 -1.7248404
## X817 -1.545331 -1.8932321
## X819 -1.489637 -1.8749213
## X821 -1.576324 -1.8208036
## X822 -1.370978 -1.8145440
## X824 -1.447752 -1.4406136
## X825 -1.442104 -1.6107907
## X826 -1.533868 -1.7675541
## X827 -1.352253 -1.5039222
## X828 -1.445111 -1.4937496
## X829 -1.313415 -1.3748365
## X830 -1.438360 -1.5629177
## X831 -1.551343 -2.0389620
## X832 -1.598480 -1.6113793
## X833 -1.621791 -1.8611765
## X834 -1.425023 -1.5267281
## X835 -1.484873 -1.7345684
## X836 -1.563074 -1.6885556
## X837 -1.660208 -2.0439127
## X838 -1.544476 -1.3314830
## X839 -1.511439 -1.9185567
## X840 -1.738456 -2.0340294
## X841 -1.502079 -1.8256936
## X842 -1.537670 -1.7575668
## X843 -1.459169 -1.7522726
## X844 -1.519671 -1.9968038
## X845 -1.501675 -2.2447636
## X846 -1.543196 -1.8083150
## X847 -1.550051 -1.9474538
## X848 -1.638076 -2.1389154
## X849 -1.575433 -1.6791818
## X851 -1.644889 -1.5915268
## X852 -1.439855 -1.3379726
## X853 -1.499252 -1.8000570
## X855 -1.643912 -1.9960060
## X856 -1.606788 -2.0282975
## X857 -1.682219 -2.1621529
## X858 -1.648811 -1.6791818
## X859 -1.605860 -1.4990925
## X860 -1.663562 -2.1959068
## X861 -1.520499 -1.6748318
## X862 -1.453060 -1.4401046
## X863 -1.497641 -1.5915268
## X864 -1.526737 -2.1091071
## X866 -1.698055 -2.3203498
## X868 -1.691083 -1.8954469
## X869 -1.594361 -2.2380872
## X871 -1.638076 -1.8597382
## X872 -1.506542 -1.4847225
## X873 -1.480137 -2.2514717
## X874 -1.593905 -2.2562827
## X875 -1.685886 -1.5114749
## X877 -1.669710 -1.6569733
## X878 -1.713449 -2.2005314
## X879 -1.654735 -2.3571020
## X880 -1.558708 -1.3869129
## X881 -1.657217 -1.9762139
## X883 -1.618012 -1.4350269
## X884 -1.506542 -1.5680169
## X885 -1.612379 -2.5679247
## X887 -1.471917 -1.7715714
## X888 -1.559579 -1.5719981
## X889 -1.747337 -2.5871077
## X890 -1.484477 -1.9163022
## X891 -1.636142 -1.6184651
## X892 -1.429075 -2.0978789
## X893 -1.412624 -0.6826914
## X894 -1.544476 -1.8771050
## X897 -1.656223 -2.2923990
## X898 -1.413348 -1.5903692
## X899 -1.475039 -1.8235956
## X900 -1.471528 -1.6399753
## X901 -1.530924 -1.3351867
## X902 -1.475821 -1.4857809
## X903 -1.583937 -1.7695612
## X904 -1.559143 -1.9574875
## X907 -1.447374 -1.2973504
## X908 -1.484477 -1.7177547
## X909 -1.440979 -1.9276134
## X910 -1.528409 -1.6196501
## X911 -1.554367 -1.6624877
## X912 -1.478172 -1.4810257
## X914 -1.461856 -1.8034913
## X915 -1.501270 -2.0538690
## X916 -1.530504 -1.7267800
## X918 -1.435005 -1.7456862
## X920 -1.664817 -1.8270941
## X921 -1.427968 -1.0696662
## X924 -1.722066 -1.9405520
## X926 -1.508171 -1.5845978
## X927 -1.589811 -2.1151915
## X929 -1.512259 -2.0373158
## X930 -1.699057 -2.2323896
## X931 -1.595732 -1.8947083
## X933 -1.510212 -2.0875956
## X934 -1.563074 -1.8201066
## X935 -1.514721 -1.9155516
## X937 -1.498044 -1.5256320
## X938 -1.500059 -1.9920239
## X939 -1.528409 -1.8201066
## X940 -1.479351 -0.8795614
## X942 -1.572324 -1.8277950
## X943 -1.423555 -1.8568664
## X944 -1.611911 -1.4694835
## X945 -1.553502 -1.5617875
## X946 -1.594361 -1.9245875
## X947 -1.610510 -1.8532856
## X948 -1.536401 -1.4365479
## X949 -1.221525 -1.1031070
## X951 -1.616599 -1.5344296
## X952 -1.725620 -2.2727630
## X954 -1.583937 -1.8235956
## X955 -1.520913 -2.0185279
## X957 -1.743107 -1.9668175
## X958 -1.613314 -2.3063064
## X959 -1.546616 -1.9405520
## X960 -1.508171 -1.6904389
## X961 -1.461856 -2.0447396
## X962 -1.381022 -1.5427374
## X963 -1.445865 -1.2325409
## X964 -1.472696 -1.6220237
## X965 -1.630368 -1.9817260
## X966 -1.474257 -1.8734676
## X968 -1.604934 -1.9298875
## X969 -1.549621 -1.7938986
## X970 -1.316639 -1.5109338
## X971 -1.495633 -2.0323892
## X972 -1.697160 -1.5316732
## X975 -1.500059 -2.2154336
## X977 -1.693330 -2.0096347
## X978 -1.453440 -1.6155076
## X979 -1.551343 -1.4025614
## X981 -1.497641 -1.6527015
## X982 -1.620845 -2.1030497
## X983 -1.625115 -1.5561527
## X984 -1.592083 -1.5174436
## X986 -1.436867 -1.7319669
## X988 -1.520085 -1.7966320
## X989 -1.533447 -1.6661779
## X990 -1.547473 -1.6303680
## X991 -1.520913 -1.7615522
## X992 -1.479351 -1.7884496
## X994 -1.486061 -1.5377457
## X995 -1.602161 -2.1265631
## X996 -1.514721 -1.6393726
## X997 -1.524652 -1.6729722
## X998 -1.647829 -2.0970190
## X999 -1.629410 -2.2983428
## X1000 -1.477780 -1.7358713
## X1001 -1.465711 -1.9559389
## X1002 -1.386615 -1.6321636
## X1003 -1.489239 -1.6472311
## X1004 -1.560888 -1.9801488
## X1005 -1.405058 -1.5471923
## X1006 -1.562636 -1.4747157
## X1007 -1.570996 -1.8313051
## X1008 -1.605860 -1.9896404
## X1009 -1.645868 -2.3274232
## X1011 -1.471139 -2.0000000
## X1012 -1.627498 -2.6386361
## X1013 -1.673109 -1.8497148
## X1014 -1.541491 -1.7516124
## X1016 -1.484873 -1.6686442
## X1017 -1.557403 -1.3333333
## X1018 -1.619427 -2.0234038
## X1019 -1.498044 -2.1996053
## X1020 -1.690457 -2.1657627
## X1021 -1.436122 -2.1766489
## X1022 -1.565266 -2.0430863
## X1024 -1.557838 -1.4974871
## X1026 -1.482896 -1.7241946
## X1027 -1.530504 -2.0455670
## X1028 -1.560888 -2.1648594
## X1029 -1.609578 -2.1513794
## X1031 -1.502484 -1.8917577
## X1032 -1.654239 -2.1300810
## X1033 -1.508579 -1.5300225
## X1035 -1.568346 -1.7496338
## X1036 -1.504916 -1.9505330
## X1037 -1.490035 -1.6172812
## X1038 -1.565705 -2.1693820
## X1039 -1.347217 -1.8778337
## X1040 -1.553935 -1.5499851
## X1041 -1.642449 -2.0790851
## X1042 -1.630847 -1.8575837
## X1044 -1.512668 -1.9367333
## X1048 -1.504510 -1.6879284
## X1049 -1.494430 -1.4720966
## X1050 -1.565266 -2.0773893
## X1051 -1.631327 -2.1204282
## X1053 -1.569228 -1.9856773
## X1054 -1.431294 -1.9551652
## X1055 -1.579897 -1.5185321
## X1056 -1.594818 -2.0364934
## X1057 -1.441353 -1.4996282
## X1058 -1.436867 -1.7769479
## X1059 -1.658213 -0.9244642
## X1062 -1.522157 -1.5076925
## X1063 -1.691396 -2.1885391
## X1064 -1.605860 -1.8583015
## X1065 -1.561761 -2.1091071
## X1067 -1.535978 -1.6303680
## X1069 -1.460319 -2.0160966
## X1070 -1.598022 -2.2864798
## X1073 -1.569228 -1.7087947
## X1074 -1.307322 -1.6285751
## X1075 -1.274705 -1.7476584
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## X1078 -1.503699 -2.1702883
## X1079 -1.363438 -1.6435978
## X1080 -1.627020 -1.9193090
## X1081 -1.596189 -2.1398020
## X1082 -1.419165 -1.3402996
## X1083 -1.558708 -1.9028570
## X1084 -1.548761 -2.1867032
## X1085 -1.453060 -1.6155076
## X1086 -1.449644 -1.6072651
## X1087 -1.478565 -1.9613670
## X1088 -1.463396 -1.9359709
## X1089 -1.456108 -1.6090266
## X1091 -1.508579 -1.5595304
## X1092 -1.610510 -1.9551652
## X1093 -1.475430 -1.7470007
## X1095 -1.395077 -1.6618738
## X1096 -1.401122 -1.3719574
## X1099 -1.438733 -1.7925342
## X1100 -1.505728 -2.0177170
## X1101 -1.427968 -1.5322240
## X1102 -1.541066 -1.7756016
## X1103 -1.615659 -1.5245369
## X1104 -1.474257 -2.1802966
## X1107 -1.351243 -1.7776215
## X1108 -1.544476 -1.6166897
## X1110 -1.507356 -2.1442434
## X1111 -1.509395 -1.5427374
## X1112 -1.633249 -1.8334159
## X1114 -1.541066 -2.2173075
## X1115 -1.561761 -1.8910211
## X1116 -1.532184 -1.8626165
## X1117 -1.461471 -1.8554329
## X1118 -1.569228 -1.9590379
## X1119 -1.567024 -1.6160985
## X1120 -1.662208 -2.0340294
## X1121 -1.620372 -1.5527846
## X1122 -1.553935 -2.0765423
## X1123 -1.612846 -2.0530365
## X1124 -1.556969 -2.1065078
## X1125 -1.491630 -2.2390390
## X1126 -1.540640 -2.2051713
## X1127 -1.627020 -2.0201513
## X1128 -1.649795 -2.2088944
## X1130 -1.550912 -2.2163702
## X1131 -1.700430 -3.0539870
## X1132 -1.478565 -1.1276737
## X1133 -1.482501 -1.6954754
## X1134 -1.481318 -2.4065265
## X1135 -1.583937 -1.9436150
## X1137 -1.391894 -1.1284389
##
## $usekernel
## [1] FALSE
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## $varnames
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## [5] "smoothness_worst" "symmetry_worst"
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## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## fL usekernel adjust
## 1 2 FALSE FALSE
##
## $obsLevels
## [1] "M" "B"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"class")
## [1] "NaiveBayes"BAL_NB_Tune$results## usekernel fL adjust ROC Sens Spec ROCSD SensSD
## 1 FALSE 2 FALSE 0.8864212 0.7576471 0.8643356 0.02628655 0.04767519
## 2 TRUE 2 FALSE NaN NaN NaN NA NA
## SpecSD
## 1 0.03462688
## 2 NA(BAL_NB_Train_AUROC <- BAL_NB_Tune$results[BAL_NB_Tune$results$usekernel==BAL_NB_Tune$bestTune$usekernel &
BAL_NB_Tune$results$adjust==BAL_NB_Tune$bestTune$adjust &
BAL_NB_Tune$results$fL==BAL_NB_Tune$bestTune$fL,
c("ROC")])## [1] 0.8864212##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_NB_Test <- data.frame(BAL_NB_Test_Observed = MA_Test$diagnosis,
BAL_NB_Test_Predicted = predict(BAL_NB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_NB_Test## BAL_NB_Test_Observed BAL_NB_Test_Predicted.M BAL_NB_Test_Predicted.B
## 8 M 9.812673e-01 0.0187326580
## 13 M 7.881578e-01 0.2118422442
## 16 M 9.997184e-01 0.0002815572
## 19 M 8.671011e-01 0.1328989311
## 24 M 7.560913e-01 0.2439087121
## 31 M 9.928321e-01 0.0071679181
## 37 M 9.719732e-01 0.0280268447
## 42 M 9.921992e-01 0.0078007626
## 44 M 9.691234e-01 0.0308765939
## 46 M 9.385286e-01 0.0614713780
## 48 M 9.924481e-01 0.0075519390
## 54 M 6.304132e-01 0.3695868316
## 61 B 2.168351e-02 0.9783164933
## 65 M 9.973897e-01 0.0026103200
## 69 B 9.547752e-01 0.0452247849
## 70 B 1.469355e-03 0.9985306451
## 71 M 1.671223e-01 0.8328777105
## 72 B 2.901232e-03 0.9970987683
## 82 B 7.848737e-01 0.2151262694
## 89 B 6.771435e-01 0.3228564506
## 93 B 1.362549e-04 0.9998637451
## 98 B 3.105533e-01 0.6894466572
## 103 B 1.169672e-02 0.9883032766
## 104 B 7.226036e-01 0.2773964211
## 107 B 9.242559e-01 0.0757441027
## 109 M 9.977118e-01 0.0022882266
## 116 B 7.041220e-01 0.2958779927
## 123 M 9.747772e-01 0.0252228348
## 125 B 1.610990e-03 0.9983890097
## 129 B 1.254316e-01 0.8745684067
## 130 M 9.654620e-01 0.0345379953
## 136 M 6.257974e-01 0.3742025777
## 139 M 7.321426e-01 0.2678574454
## 147 M 9.976976e-01 0.0023024442
## 149 B 3.341167e-02 0.9665883337
## 150 B 2.292809e-03 0.9977071911
## 162 M 3.374330e-03 0.9966256701
## 167 B 1.619263e-06 0.9999983807
## 185 M 5.289888e-01 0.4710111653
## 191 M 9.997987e-01 0.0002013151
## 194 M 9.979348e-01 0.0020651590
## 197 M 9.948181e-01 0.0051819440
## 200 M 9.940340e-01 0.0059659804
## 201 B 5.432820e-01 0.4567179767
## 209 B 9.864943e-01 0.0135057292
## 211 M 5.680945e-01 0.4319054783
## 213 M 1.159697e-01 0.8840303444
## 217 B 8.273550e-01 0.1726450444
## 221 B 9.337968e-04 0.9990662032
## 222 B 1.921628e-02 0.9807837153
## 223 B 2.752034e-01 0.7247966362
## 228 B 7.988226e-03 0.9920117738
## 237 M 9.746734e-01 0.0253266139
## 239 B 5.506258e-01 0.4493741790
## 242 B 5.225347e-05 0.9999477465
## 246 B 7.820348e-01 0.2179652002
## 256 M 5.729148e-01 0.4270851809
## 259 M 9.899523e-01 0.0100476752
## 262 M 1.828597e-01 0.8171403353
## 263 M 5.831366e-01 0.4168633816
## 266 M 9.423911e-01 0.0576089409
## 272 B 7.236743e-04 0.9992763257
## 274 B 1.865623e-02 0.9813437674
## 275 M 4.477037e-01 0.5522963242
## 285 B 1.283059e-03 0.9987169411
## 300 B 1.452065e-01 0.8547934717
## 308 B 7.261562e-06 0.9999927384
## 328 B 2.634175e-04 0.9997365825
## 345 B 2.222892e-02 0.9777710828
## 349 B 1.684737e-02 0.9831526336
## 356 B 4.814841e-02 0.9518515907
## 363 B 2.610626e-01 0.7389373556
## 365 B 4.974638e-03 0.9950253624
## 368 B 2.272191e-01 0.7727808560
## 382 B 4.210317e-03 0.9957896827
## 383 B 1.044271e-02 0.9895572919
## 387 B 9.190923e-04 0.9990809077
## 388 B 6.015690e-05 0.9999398431
## 401 M 9.937431e-01 0.0062568916
## 403 B 1.523278e-02 0.9847672192
## 417 B 8.998595e-01 0.1001405044
## 420 B 3.812498e-01 0.6187501980
## 428 B 6.881346e-01 0.3118654269
## 434 M 9.404447e-01 0.0595552690
## 442 M 8.579651e-01 0.1420348925
## 444 B 4.442142e-03 0.9955578580
## 445 M 3.409643e-02 0.9659035700
## 454 B 3.366251e-03 0.9966337488
## 455 B 1.320695e-02 0.9867930530
## 460 B 1.629774e-01 0.8370226494
## 462 M 9.555680e-01 0.0444320351
## 463 B 2.543201e-02 0.9745679909
## 472 B 2.461675e-01 0.7538324803
## 484 B 1.534474e-02 0.9846552632
## 489 B 1.607416e-01 0.8392584112
## 493 M 7.806461e-01 0.2193538741
## 494 B 2.870437e-06 0.9999971296
## 497 B 7.165893e-01 0.2834107269
## 498 B 7.976398e-02 0.9202360221
## 501 B 1.444296e-02 0.9855570434
## 502 M 9.988572e-01 0.0011427954
## 507 B 6.060617e-01 0.3939382969
## 509 B 3.659993e-03 0.9963400073
## 525 B 8.206683e-02 0.9179331705
## 526 B 3.149118e-02 0.9685088175
## 527 B 8.699125e-01 0.1300875489
## 531 B 3.977364e-01 0.6022636293
## 532 B 8.885794e-01 0.1114206422
## 534 M 4.729801e-01 0.5270199251
## 537 M 9.182135e-01 0.0817865391
## 544 B 3.039975e-01 0.6960024976
## 546 B 4.950886e-01 0.5049114198
## 548 B 1.351934e-01 0.8648065921
## 550 B 4.716409e-01 0.5283590575
## 551 B 3.146255e-03 0.9968537448
## 556 B 8.591647e-01 0.1408352922
## 557 B 1.816539e-01 0.8183460835
## 575 M 9.765204e-01 0.0234796380
## 578 M 9.993858e-01 0.0006142344
## 581 M 9.190702e-01 0.0809297869
## 583 M 3.392679e-01 0.6607320744
## 589 B 2.089111e-02 0.9791088898
## 590 B 1.214756e-01 0.8785244318
## 601 M 9.982402e-01 0.0017598082
## 603 M 9.887012e-01 0.0112988498
## 611 M 9.921992e-01 0.0078007626
## 617 M 9.924481e-01 0.0075519390
## 619 B 2.963917e-01 0.7036083437
## 625 B 6.499918e-02 0.9350008165
## 628 B 1.393959e-03 0.9986060411
## 632 M 9.742588e-01 0.0257411818
## 646 B 2.993435e-04 0.9997006565
## 649 B 2.036427e-01 0.7963572964
## 657 M 9.294722e-01 0.0705278433
## 662 B 1.362549e-04 0.9998637451
## 665 M 9.244672e-01 0.0755327874
## 677 B 4.899191e-02 0.9510080946
## 679 B 7.539674e-01 0.2460325815
## 685 B 7.041220e-01 0.2958779927
## 687 M 9.800610e-01 0.0199390377
## 689 M 4.952652e-01 0.5047347899
## 695 B 5.323889e-03 0.9946761114
## 701 M 6.978591e-01 0.3021408704
## 704 M 8.173221e-01 0.1826778995
## 706 B 1.619121e-01 0.8380878502
## 709 B 6.283883e-03 0.9937161170
## 715 B 4.234854e-02 0.9576514646
## 726 M 7.962994e-01 0.2037006399
## 734 M 7.155837e-01 0.2844163454
## 747 M 8.794790e-01 0.1205210336
## 752 M 9.340052e-01 0.0659947616
## 763 M 9.979348e-01 0.0020651590
## 765 B 5.775093e-03 0.9942249074
## 775 M 1.718460e-01 0.8281540071
## 780 M 5.680945e-01 0.4319054783
## 786 B 8.273550e-01 0.1726450444
## 792 B 2.752034e-01 0.7247966362
## 796 B 6.297508e-03 0.9937024917
## 809 M 9.731131e-01 0.0268868939
## 813 B 5.222499e-02 0.9477750071
## 816 B 7.849621e-03 0.9921503793
## 818 B 9.291527e-01 0.0708472692
## 820 M 8.356248e-01 0.1643752263
## 823 M 3.733182e-01 0.6266817630
## 850 M 9.957456e-01 0.0042544435
## 854 B 1.283059e-03 0.9987169411
## 865 B 1.545147e-04 0.9998454853
## 867 M 1.965801e-02 0.9803419942
## 870 M 8.832716e-01 0.1167284359
## 876 B 7.248908e-04 0.9992751092
## 882 B 7.471474e-04 0.9992528526
## 886 B 3.482215e-06 0.9999965178
## 895 B 3.659113e-02 0.9634088692
## 896 B 4.521132e-04 0.9995478868
## 905 M 8.848879e-01 0.1151120607
## 906 B 1.450107e-03 0.9985498928
## 913 M 9.819396e-01 0.0180604337
## 917 B 3.754428e-03 0.9962455719
## 919 B 5.127009e-02 0.9487299131
## 922 M 9.370673e-01 0.0629327261
## 923 M 9.748852e-01 0.0251147663
## 925 B 4.814841e-02 0.9518515907
## 928 B 8.699658e-04 0.9991300342
## 932 B 2.610626e-01 0.7389373556
## 936 M 9.726773e-01 0.0273226617
## 941 B 5.017463e-05 0.9999498254
## 950 B 1.730492e-01 0.8269507532
## 953 B 5.039053e-01 0.4960947100
## 956 B 9.190923e-04 0.9990809077
## 967 B 3.855968e-03 0.9961440318
## 973 B 6.279003e-02 0.9372099672
## 974 B 7.967346e-05 0.9999203265
## 976 B 4.407911e-03 0.9955920886
## 980 B 3.506288e-01 0.6493711703
## 985 B 8.606275e-01 0.1393724772
## 987 M 9.539449e-01 0.0460550548
## 993 B 1.773299e-01 0.8226700764
## 1010 B 3.395580e-01 0.6604419928
## 1015 B 8.060954e-01 0.1939045871
## 1023 B 3.366251e-03 0.9966337488
## 1025 B 5.667270e-01 0.4332729894
## 1030 M 9.805921e-01 0.0194078603
## 1034 B 9.308926e-03 0.9906910739
## 1043 B 2.092386e-02 0.9790761384
## 1045 B 2.280148e-02 0.9771985228
## 1046 B 2.637147e-01 0.7362852638
## 1047 B 1.747931e-04 0.9998252069
## 1052 B 2.553411e-02 0.9744658931
## 1060 B 3.113604e-01 0.6886396188
## 1061 B 2.238129e-05 0.9999776187
## 1066 B 7.165893e-01 0.2834107269
## 1068 M 3.128620e-01 0.6871380447
## 1071 M 9.988572e-01 0.0011427954
## 1072 B 4.139866e-01 0.5860134296
## 1077 B 5.430217e-01 0.4569782738
## 1090 B 5.656148e-01 0.4343852029
## 1094 B 8.206683e-02 0.9179331705
## 1097 B 6.804212e-04 0.9993195788
## 1098 B 5.610047e-03 0.9943899527
## 1105 M 7.089731e-01 0.2910269275
## 1106 M 9.182135e-01 0.0817865391
## 1109 B 9.474609e-01 0.0525390867
## 1113 B 3.039975e-01 0.6960024976
## 1129 B 8.250674e-01 0.1749326039
## 1136 M 5.683597e-01 0.4316402792
## 1138 B 8.801571e-06 0.9999911984##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_NB_Test_ROC <- roc(response = BAL_NB_Test$BAL_NB_Test_Observed,
predictor = BAL_NB_Test$BAL_NB_Test_Predicted.M,
levels = rev(levels(BAL_NB_Test$BAL_NB_Test_Observed)))
(BAL_NB_Test_AUROC <- auc(BAL_NB_Test_ROC)[1])## [1] 0.90383971.7.6 Ensemble Learner Model Development using Linear Regression (ENL)
Details.
Code Chunk | Output
##################################
# Consolidating the base learners
# with optimal hyperparameters
##################################
set.seed(12345678)
BAL_LIST <- caretList(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
trControl=RKFold_Control,
metric="ROC",
tuneList=list(
BAL_LDA=caretModelSpec(method="lda",
preProcess=c("center","scale")),
BAL_CART=caretModelSpec(method="rpart",
tuneGrid=data.frame(cp=0.001)),
BAL_SVM_R=caretModelSpec(method="svmRadial",
preProcess=c("center","scale"),
tuneGrid=data.frame(C = 2048, sigma = 0.1790538)),
BAL_KNN=caretModelSpec(method="knn",
preProcess=c("center","scale"),
tuneGrid=data.frame(k = 1)),
BAL_NB=caretModelSpec(method="nb",
tuneGrid=data.frame(usekernel=FALSE,fL = 2,adjust = FALSE)))
)
BAL_LIST## $BAL_LDA
## Linear Discriminant Analysis
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.87416 0.6994118 0.886029
##
##
## $BAL_CART
## CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8725597 0.7741176 0.8531411
##
## Tuning parameter 'cp' was held constant at a value of 0.001
##
## $BAL_SVM_R
## Support Vector Machines with Radial Basis Function Kernel
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9081946 0.8058824 0.9272616
##
## Tuning parameter 'sigma' was held constant at a value of 0.1790538
##
## Tuning parameter 'C' was held constant at a value of 2048
##
## $BAL_KNN
## k-Nearest Neighbors
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8981405 0.8882353 0.9080458
##
## Tuning parameter 'k' was held constant at a value of 1
##
## $BAL_NB
## Naive Bayes
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8869568 0.7582353 0.8643143
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'usekernel' was held constant at a value of FALSE
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
##
## attr(,"class")
## [1] "caretList"##################################
# Comparing the base learners
# with optimal hyperparameters
##################################
BAL_LIST_RESAMPLES <- resamples(BAL_LIST)
summary(BAL_LIST_RESAMPLES)##
## Call:
## summary.resamples(object = BAL_LIST_RESAMPLES)
##
## Models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8363171 0.8556266 0.8728070 0.8741600 0.8872549 0.9164962 0
## BAL_CART 0.7928277 0.8517157 0.8767415 0.8725597 0.8908669 0.9225703 0
## BAL_SVM_R 0.8537152 0.8934469 0.9063939 0.9081946 0.9286636 0.9615583 0
## BAL_KNN 0.8582481 0.8863171 0.8900256 0.8981405 0.9080563 0.9472394 0
## BAL_NB 0.8476522 0.8715170 0.8891899 0.8869568 0.9057018 0.9246803 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.6029412 0.6617647 0.7058824 0.6994118 0.7352941 0.8235294 0
## BAL_CART 0.6764706 0.7500000 0.7794118 0.7741176 0.8088235 0.8823529 0
## BAL_SVM_R 0.7205882 0.7794118 0.8088235 0.8058824 0.8235294 0.8823529 0
## BAL_KNN 0.7941176 0.8529412 0.8823529 0.8882353 0.9264706 0.9558824 0
## BAL_NB 0.6764706 0.7205882 0.7352941 0.7582353 0.8088235 0.8529412 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8347826 0.8684211 0.8859649 0.8860290 0.9035088 0.9391304 0
## BAL_CART 0.7807018 0.8245614 0.8608696 0.8531411 0.8869565 0.9035088 0
## BAL_SVM_R 0.8508772 0.9122807 0.9298246 0.9272616 0.9473684 0.9736842 0
## BAL_KNN 0.8333333 0.8869565 0.9043478 0.9080458 0.9304348 0.9565217 0
## BAL_NB 0.7982456 0.8421053 0.8596491 0.8643143 0.8859649 0.9304348 0dotplot(BAL_LIST_RESAMPLES)
splom(BAL_LIST_RESAMPLES)
##################################
# Measuring the correlation among
# base learners
##################################
BAL_LIST_COR <- modelCor(resamples(BAL_LIST))
##################################
# Formulating an ensemble model
# using the base learners
##################################
set.seed(12345678)
ENL <- caretEnsemble(BAL_LIST,
metric="ROC",
trControl=RKFold_Control)
print(ENL)## A glm ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Generalized Linear Model
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9476578 0.8858824 0.922028(ENL_Train_AUROC <- ENL$ens_model$results$ROC)## [1] 0.9476578##################################
# Independently evaluating the model
# on the test set
##################################
ENL_Test <- data.frame(ENL_Test_Observed = MA_Test$diagnosis,
ENL_Test_Predicted = predict(ENL,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
ENL_Test$ENL_Test_Predicted.M <- ENL_Test$ENL_Test_Predicted
ENL_Test## ENL_Test_Observed ENL_Test_Predicted ENL_Test_Predicted.M
## 1 M 0.93698484 0.93698484
## 2 M 0.90316238 0.90316238
## 3 M 0.97601568 0.97601568
## 4 M 0.91077849 0.91077849
## 5 M 0.93437192 0.93437192
## 6 M 0.96912348 0.96912348
## 7 M 0.94640442 0.94640442
## 8 M 0.86974627 0.86974627
## 9 M 0.96390978 0.96390978
## 10 M 0.97013507 0.97013507
## 11 M 0.97048299 0.97048299
## 12 M 0.80221002 0.80221002
## 13 B 0.02124624 0.02124624
## 14 M 0.97377786 0.97377786
## 15 B 0.30541332 0.30541332
## 16 B 0.02099828 0.02099828
## 17 M 0.71594387 0.71594387
## 18 B 0.02056671 0.02056671
## 19 B 0.26152217 0.26152217
## 20 B 0.12122490 0.12122490
## 21 B 0.02050352 0.02050352
## 22 B 0.04257131 0.04257131
## 23 B 0.04396013 0.04396013
## 24 B 0.14459123 0.14459123
## 25 B 0.20488627 0.20488627
## 26 M 0.93524820 0.93524820
## 27 B 0.86514117 0.86514117
## 28 M 0.93285449 0.93285449
## 29 B 0.02085434 0.02085434
## 30 B 0.02510866 0.02510866
## 31 M 0.92653274 0.92653274
## 32 M 0.91582476 0.91582476
## 33 M 0.81037042 0.81037042
## 34 M 0.94862798 0.94862798
## 35 B 0.05990661 0.05990661
## 36 B 0.02129373 0.02129373
## 37 M 0.56743637 0.56743637
## 38 B 0.02059139 0.02059139
## 39 M 0.88679401 0.88679401
## 40 M 0.95930601 0.95930601
## 41 M 0.97588417 0.97588417
## 42 M 0.96579927 0.96579927
## 43 M 0.97560484 0.97560484
## 44 B 0.13604412 0.13604412
## 45 B 0.31905334 0.31905334
## 46 M 0.87024068 0.87024068
## 47 M 0.70472111 0.70472111
## 48 B 0.84716678 0.84716678
## 49 B 0.02517130 0.02517130
## 50 B 0.02453744 0.02453744
## 51 B 0.03493350 0.03493350
## 52 B 0.04507674 0.04507674
## 53 M 0.93284071 0.93284071
## 54 B 0.08406829 0.08406829
## 55 B 0.02052591 0.02052591
## 56 B 0.15755656 0.15755656
## 57 M 0.87275682 0.87275682
## 58 M 0.95026912 0.95026912
## 59 M 0.63082316 0.63082316
## 60 M 0.86809829 0.86809829
## 61 M 0.92185725 0.92185725
## 62 B 0.02052229 0.02052229
## 63 B 0.04423209 0.04423209
## 64 M 0.90013796 0.90013796
## 65 B 0.02649049 0.02649049
## 66 B 0.03040485 0.03040485
## 67 B 0.02051536 0.02051536
## 68 B 0.02050666 0.02050666
## 69 B 0.02126592 0.02126592
## 70 B 0.02614123 0.02614123
## 71 B 0.03134642 0.03134642
## 72 B 0.11493979 0.11493979
## 73 B 0.04325631 0.04325631
## 74 B 0.06191254 0.06191254
## 75 B 0.02227528 0.02227528
## 76 B 0.04325307 0.04325307
## 77 B 0.02322909 0.02322909
## 78 B 0.02052967 0.02052967
## 79 M 0.93484196 0.93484196
## 80 B 0.04345233 0.04345233
## 81 B 0.20634108 0.20634108
## 82 B 0.08299901 0.08299901
## 83 B 0.10948146 0.10948146
## 84 M 0.94731607 0.94731607
## 85 M 0.91087710 0.91087710
## 86 B 0.04080377 0.04080377
## 87 M 0.53269647 0.53269647
## 88 B 0.02061297 0.02061297
## 89 B 0.04383689 0.04383689
## 90 B 0.05612948 0.05612948
## 91 M 0.96382289 0.96382289
## 92 B 0.02332053 0.02332053
## 93 B 0.03470112 0.03470112
## 94 B 0.02244913 0.02244913
## 95 B 0.02845368 0.02845368
## 96 M 0.89267185 0.89267185
## 97 B 0.02049774 0.02049774
## 98 B 0.93276791 0.93276791
## 99 B 0.04884028 0.04884028
## 100 B 0.02392557 0.02392557
## 101 M 0.97541992 0.97541992
## 102 B 0.12656999 0.12656999
## 103 B 0.02364341 0.02364341
## 104 B 0.60219376 0.60219376
## 105 B 0.03038320 0.03038320
## 106 B 0.29505551 0.29505551
## 107 B 0.08107371 0.08107371
## 108 B 0.37623490 0.37623490
## 109 M 0.86105870 0.86105870
## 110 M 0.96287979 0.96287979
## 111 B 0.11154821 0.11154821
## 112 B 0.16941544 0.16941544
## 113 B 0.05579544 0.05579544
## 114 B 0.07386931 0.07386931
## 115 B 0.02282196 0.02282196
## 116 B 0.17065261 0.17065261
## 117 B 0.04008923 0.04008923
## 118 M 0.95269855 0.95269855
## 119 M 0.97534468 0.97534468
## 120 M 0.91998540 0.91998540
## 121 M 0.78809492 0.78809492
## 122 B 0.02246830 0.02246830
## 123 B 0.02679706 0.02679706
## 124 M 0.97562896 0.97562896
## 125 M 0.96156820 0.96156820
## 126 M 0.86974627 0.86974627
## 127 M 0.97048299 0.97048299
## 128 B 0.06944286 0.06944286
## 129 B 0.02713114 0.02713114
## 130 B 0.02072300 0.02072300
## 131 M 0.92567944 0.92567944
## 132 B 0.06590589 0.06590589
## 133 B 0.04786883 0.04786883
## 134 M 0.92226876 0.92226876
## 135 B 0.02050352 0.02050352
## 136 M 0.92166295 0.92166295
## 137 B 0.04646474 0.04646474
## 138 B 0.20432795 0.20432795
## 139 B 0.86514117 0.86514117
## 140 M 0.94355480 0.94355480
## 141 M 0.83853249 0.83853249
## 142 B 0.02496440 0.02496440
## 143 M 0.80604979 0.80604979
## 144 M 0.91402925 0.91402925
## 145 B 0.05577617 0.05577617
## 146 B 0.02069193 0.02069193
## 147 B 0.02241456 0.02241456
## 148 M 0.87189250 0.87189250
## 149 M 0.89732060 0.89732060
## 150 M 0.87656130 0.87656130
## 151 M 0.95600731 0.95600731
## 152 M 0.97588417 0.97588417
## 153 B 0.04331679 0.04331679
## 154 M 0.77608179 0.77608179
## 155 M 0.87024068 0.87024068
## 156 B 0.84716678 0.84716678
## 157 B 0.03493350 0.03493350
## 158 B 0.02072247 0.02072247
## 159 M 0.92552933 0.92552933
## 160 B 0.02877609 0.02877609
## 161 B 0.02304007 0.02304007
## 162 B 0.31638845 0.31638845
## 163 M 0.93016447 0.93016447
## 164 M 0.55664617 0.55664617
## 165 M 0.97513038 0.97513038
## 166 B 0.02649049 0.02649049
## 167 B 0.02050233 0.02050233
## 168 M 0.56291502 0.56291502
## 169 M 0.92497981 0.92497981
## 170 B 0.02158747 0.02158747
## 171 B 0.04490583 0.04490583
## 172 B 0.02049772 0.02049772
## 173 B 0.02259309 0.02259309
## 174 B 0.02134849 0.02134849
## 175 M 0.86089019 0.86089019
## 176 B 0.04511237 0.04511237
## 177 M 0.96967602 0.96967602
## 178 B 0.02078233 0.02078233
## 179 B 0.02470231 0.02470231
## 180 M 0.92219409 0.92219409
## 181 M 0.96567901 0.96567901
## 182 B 0.03134642 0.03134642
## 183 B 0.04490922 0.04490922
## 184 B 0.11493979 0.11493979
## 185 M 0.94184407 0.94184407
## 186 B 0.02049983 0.02049983
## 187 B 0.05675360 0.05675360
## 188 B 0.09446704 0.09446704
## 189 B 0.02322909 0.02322909
## 190 B 0.04266479 0.04266479
## 191 B 0.02298553 0.02298553
## 192 B 0.02050079 0.02050079
## 193 B 0.02120569 0.02120569
## 194 B 0.07527884 0.07527884
## 195 B 0.45197849 0.45197849
## 196 M 0.93061325 0.93061325
## 197 B 0.14664425 0.14664425
## 198 B 0.05185987 0.05185987
## 199 B 0.25198837 0.25198837
## 200 B 0.02061297 0.02061297
## 201 B 0.11863038 0.11863038
## 202 M 0.96913088 0.96913088
## 203 B 0.02082299 0.02082299
## 204 B 0.02419760 0.02419760
## 205 B 0.03477747 0.03477747
## 206 B 0.10145534 0.10145534
## 207 B 0.02109181 0.02109181
## 208 B 0.02138614 0.02138614
## 209 B 0.09539717 0.09539717
## 210 B 0.02050890 0.02050890
## 211 B 0.93276791 0.93276791
## 212 M 0.65252829 0.65252829
## 213 M 0.97541992 0.97541992
## 214 B 0.04558308 0.04558308
## 215 B 0.11872470 0.11872470
## 216 B 0.18484289 0.18484289
## 217 B 0.60219376 0.60219376
## 218 B 0.02052025 0.02052025
## 219 B 0.02051262 0.02051262
## 220 M 0.87701919 0.87701919
## 221 M 0.96287979 0.96287979
## 222 B 0.25378926 0.25378926
## 223 B 0.11154821 0.11154821
## 224 B 0.17287149 0.17287149
## 225 M 0.84597953 0.84597953
## 226 B 0.02427533 0.02427533#################################
# Reporting the independent evaluation results
# for the test set
#################################
ENL_Test_ROC <- roc(response = ENL_Test$ENL_Test_Observed,
predictor = ENL_Test$ENL_Test_Predicted.M,
levels = rev(levels(ENL_Test$ENL_Test_Observed)))
(ENL_Test_AUROC <- auc(ENL_Test_ROC)[1])## [1] 0.98625081.7.7 Meta Learner Model Development using Linear Regression (MEL_LR)
Details.
Code Chunk | Output
##################################
# Formulating a stacked model
# using the base learners
# and a linear regression meta-model
##################################
set.seed(12345678)
MEL_LR <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="glm")
print(MEL_LR)## A glm ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Generalized Linear Model
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9476578 0.8858824 0.922028(MEL_LR_Train_AUROC <- MEL_LR$ens_model$results$ROC)## [1] 0.9476578##################################
# Independently evaluating the model
# on the test set
##################################
MEL_LR_Test <- data.frame(MEL_LR_Test_Observed = MA_Test$diagnosis,
MEL_LR_Test_Predicted = predict(MEL_LR,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
MEL_LR_Test$MEL_LR_Test_Predicted.M <- MEL_LR_Test$MEL_LR_Test_Predicted
MEL_LR_Test## MEL_LR_Test_Observed MEL_LR_Test_Predicted MEL_LR_Test_Predicted.M
## 1 M 0.93698484 0.93698484
## 2 M 0.90316238 0.90316238
## 3 M 0.97601568 0.97601568
## 4 M 0.91077849 0.91077849
## 5 M 0.93437192 0.93437192
## 6 M 0.96912348 0.96912348
## 7 M 0.94640442 0.94640442
## 8 M 0.86974627 0.86974627
## 9 M 0.96390978 0.96390978
## 10 M 0.97013507 0.97013507
## 11 M 0.97048299 0.97048299
## 12 M 0.80221002 0.80221002
## 13 B 0.02124624 0.02124624
## 14 M 0.97377786 0.97377786
## 15 B 0.30541332 0.30541332
## 16 B 0.02099828 0.02099828
## 17 M 0.71594387 0.71594387
## 18 B 0.02056671 0.02056671
## 19 B 0.26152217 0.26152217
## 20 B 0.12122490 0.12122490
## 21 B 0.02050352 0.02050352
## 22 B 0.04257131 0.04257131
## 23 B 0.04396013 0.04396013
## 24 B 0.14459123 0.14459123
## 25 B 0.20488627 0.20488627
## 26 M 0.93524820 0.93524820
## 27 B 0.86514117 0.86514117
## 28 M 0.93285449 0.93285449
## 29 B 0.02085434 0.02085434
## 30 B 0.02510866 0.02510866
## 31 M 0.92653274 0.92653274
## 32 M 0.91582476 0.91582476
## 33 M 0.81037042 0.81037042
## 34 M 0.94862798 0.94862798
## 35 B 0.05990661 0.05990661
## 36 B 0.02129373 0.02129373
## 37 M 0.56743637 0.56743637
## 38 B 0.02059139 0.02059139
## 39 M 0.88679401 0.88679401
## 40 M 0.95930601 0.95930601
## 41 M 0.97588417 0.97588417
## 42 M 0.96579927 0.96579927
## 43 M 0.97560484 0.97560484
## 44 B 0.13604412 0.13604412
## 45 B 0.31905334 0.31905334
## 46 M 0.87024068 0.87024068
## 47 M 0.70472111 0.70472111
## 48 B 0.84716678 0.84716678
## 49 B 0.02517130 0.02517130
## 50 B 0.02453744 0.02453744
## 51 B 0.03493350 0.03493350
## 52 B 0.04507674 0.04507674
## 53 M 0.93284071 0.93284071
## 54 B 0.08406829 0.08406829
## 55 B 0.02052591 0.02052591
## 56 B 0.15755656 0.15755656
## 57 M 0.87275682 0.87275682
## 58 M 0.95026912 0.95026912
## 59 M 0.63082316 0.63082316
## 60 M 0.86809829 0.86809829
## 61 M 0.92185725 0.92185725
## 62 B 0.02052229 0.02052229
## 63 B 0.04423209 0.04423209
## 64 M 0.90013796 0.90013796
## 65 B 0.02649049 0.02649049
## 66 B 0.03040485 0.03040485
## 67 B 0.02051536 0.02051536
## 68 B 0.02050666 0.02050666
## 69 B 0.02126592 0.02126592
## 70 B 0.02614123 0.02614123
## 71 B 0.03134642 0.03134642
## 72 B 0.11493979 0.11493979
## 73 B 0.04325631 0.04325631
## 74 B 0.06191254 0.06191254
## 75 B 0.02227528 0.02227528
## 76 B 0.04325307 0.04325307
## 77 B 0.02322909 0.02322909
## 78 B 0.02052967 0.02052967
## 79 M 0.93484196 0.93484196
## 80 B 0.04345233 0.04345233
## 81 B 0.20634108 0.20634108
## 82 B 0.08299901 0.08299901
## 83 B 0.10948146 0.10948146
## 84 M 0.94731607 0.94731607
## 85 M 0.91087710 0.91087710
## 86 B 0.04080377 0.04080377
## 87 M 0.53269647 0.53269647
## 88 B 0.02061297 0.02061297
## 89 B 0.04383689 0.04383689
## 90 B 0.05612948 0.05612948
## 91 M 0.96382289 0.96382289
## 92 B 0.02332053 0.02332053
## 93 B 0.03470112 0.03470112
## 94 B 0.02244913 0.02244913
## 95 B 0.02845368 0.02845368
## 96 M 0.89267185 0.89267185
## 97 B 0.02049774 0.02049774
## 98 B 0.93276791 0.93276791
## 99 B 0.04884028 0.04884028
## 100 B 0.02392557 0.02392557
## 101 M 0.97541992 0.97541992
## 102 B 0.12656999 0.12656999
## 103 B 0.02364341 0.02364341
## 104 B 0.60219376 0.60219376
## 105 B 0.03038320 0.03038320
## 106 B 0.29505551 0.29505551
## 107 B 0.08107371 0.08107371
## 108 B 0.37623490 0.37623490
## 109 M 0.86105870 0.86105870
## 110 M 0.96287979 0.96287979
## 111 B 0.11154821 0.11154821
## 112 B 0.16941544 0.16941544
## 113 B 0.05579544 0.05579544
## 114 B 0.07386931 0.07386931
## 115 B 0.02282196 0.02282196
## 116 B 0.17065261 0.17065261
## 117 B 0.04008923 0.04008923
## 118 M 0.95269855 0.95269855
## 119 M 0.97534468 0.97534468
## 120 M 0.91998540 0.91998540
## 121 M 0.78809492 0.78809492
## 122 B 0.02246830 0.02246830
## 123 B 0.02679706 0.02679706
## 124 M 0.97562896 0.97562896
## 125 M 0.96156820 0.96156820
## 126 M 0.86974627 0.86974627
## 127 M 0.97048299 0.97048299
## 128 B 0.06944286 0.06944286
## 129 B 0.02713114 0.02713114
## 130 B 0.02072300 0.02072300
## 131 M 0.92567944 0.92567944
## 132 B 0.06590589 0.06590589
## 133 B 0.04786883 0.04786883
## 134 M 0.92226876 0.92226876
## 135 B 0.02050352 0.02050352
## 136 M 0.92166295 0.92166295
## 137 B 0.04646474 0.04646474
## 138 B 0.20432795 0.20432795
## 139 B 0.86514117 0.86514117
## 140 M 0.94355480 0.94355480
## 141 M 0.83853249 0.83853249
## 142 B 0.02496440 0.02496440
## 143 M 0.80604979 0.80604979
## 144 M 0.91402925 0.91402925
## 145 B 0.05577617 0.05577617
## 146 B 0.02069193 0.02069193
## 147 B 0.02241456 0.02241456
## 148 M 0.87189250 0.87189250
## 149 M 0.89732060 0.89732060
## 150 M 0.87656130 0.87656130
## 151 M 0.95600731 0.95600731
## 152 M 0.97588417 0.97588417
## 153 B 0.04331679 0.04331679
## 154 M 0.77608179 0.77608179
## 155 M 0.87024068 0.87024068
## 156 B 0.84716678 0.84716678
## 157 B 0.03493350 0.03493350
## 158 B 0.02072247 0.02072247
## 159 M 0.92552933 0.92552933
## 160 B 0.02877609 0.02877609
## 161 B 0.02304007 0.02304007
## 162 B 0.31638845 0.31638845
## 163 M 0.93016447 0.93016447
## 164 M 0.55664617 0.55664617
## 165 M 0.97513038 0.97513038
## 166 B 0.02649049 0.02649049
## 167 B 0.02050233 0.02050233
## 168 M 0.56291502 0.56291502
## 169 M 0.92497981 0.92497981
## 170 B 0.02158747 0.02158747
## 171 B 0.04490583 0.04490583
## 172 B 0.02049772 0.02049772
## 173 B 0.02259309 0.02259309
## 174 B 0.02134849 0.02134849
## 175 M 0.86089019 0.86089019
## 176 B 0.04511237 0.04511237
## 177 M 0.96967602 0.96967602
## 178 B 0.02078233 0.02078233
## 179 B 0.02470231 0.02470231
## 180 M 0.92219409 0.92219409
## 181 M 0.96567901 0.96567901
## 182 B 0.03134642 0.03134642
## 183 B 0.04490922 0.04490922
## 184 B 0.11493979 0.11493979
## 185 M 0.94184407 0.94184407
## 186 B 0.02049983 0.02049983
## 187 B 0.05675360 0.05675360
## 188 B 0.09446704 0.09446704
## 189 B 0.02322909 0.02322909
## 190 B 0.04266479 0.04266479
## 191 B 0.02298553 0.02298553
## 192 B 0.02050079 0.02050079
## 193 B 0.02120569 0.02120569
## 194 B 0.07527884 0.07527884
## 195 B 0.45197849 0.45197849
## 196 M 0.93061325 0.93061325
## 197 B 0.14664425 0.14664425
## 198 B 0.05185987 0.05185987
## 199 B 0.25198837 0.25198837
## 200 B 0.02061297 0.02061297
## 201 B 0.11863038 0.11863038
## 202 M 0.96913088 0.96913088
## 203 B 0.02082299 0.02082299
## 204 B 0.02419760 0.02419760
## 205 B 0.03477747 0.03477747
## 206 B 0.10145534 0.10145534
## 207 B 0.02109181 0.02109181
## 208 B 0.02138614 0.02138614
## 209 B 0.09539717 0.09539717
## 210 B 0.02050890 0.02050890
## 211 B 0.93276791 0.93276791
## 212 M 0.65252829 0.65252829
## 213 M 0.97541992 0.97541992
## 214 B 0.04558308 0.04558308
## 215 B 0.11872470 0.11872470
## 216 B 0.18484289 0.18484289
## 217 B 0.60219376 0.60219376
## 218 B 0.02052025 0.02052025
## 219 B 0.02051262 0.02051262
## 220 M 0.87701919 0.87701919
## 221 M 0.96287979 0.96287979
## 222 B 0.25378926 0.25378926
## 223 B 0.11154821 0.11154821
## 224 B 0.17287149 0.17287149
## 225 M 0.84597953 0.84597953
## 226 B 0.02427533 0.02427533#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_LR_Test_ROC <- roc(response = MEL_LR_Test$MEL_LR_Test_Observed,
predictor = MEL_LR_Test$MEL_LR_Test_Predicted.M,
levels = rev(levels(MEL_LR_Test$MEL_LR_Test_Observed)))
(MEL_LR_Test_AUROC <- auc(MEL_LR_Test_ROC)[1])## [1] 0.98625081.7.8 Meta Learner Model Development using Random Forest (MEL_RF)
Details.
Code Chunk | Output
##################################
# Formulating a stacked model
# using the base learners
# and a random forest meta-model
##################################
set.seed(12345678)
MEL_RF <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="rf")
print(MEL_RF)## A rf ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Random Forest
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 2 0.9807114 0.9345882 0.9550350
## 3 0.9807720 0.9335294 0.9555245
## 5 0.9797318 0.9323529 0.9537762
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 3.(MEL_RF_Train_AUROC <- max(MEL_RF$ens_model$results$ROC))## [1] 0.980772##################################
# Independently evaluating the model
# on the test set
##################################
MEL_RF_Test <- data.frame(MEL_RF_Test_Observed = MA_Test$diagnosis,
MEL_RF_Test_Predicted = predict(MEL_RF,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
MEL_RF_Test$MEL_RF_Test_Predicted.M <- MEL_RF_Test$MEL_RF_Test_Predicted
MEL_RF_Test## MEL_RF_Test_Observed MEL_RF_Test_Predicted MEL_RF_Test_Predicted.M
## 1 M 1.000 1.000
## 2 M 0.982 0.982
## 3 M 1.000 1.000
## 4 M 0.950 0.950
## 5 M 0.528 0.528
## 6 M 1.000 1.000
## 7 M 0.976 0.976
## 8 M 0.884 0.884
## 9 M 1.000 1.000
## 10 M 0.924 0.924
## 11 M 0.902 0.902
## 12 M 0.950 0.950
## 13 B 0.036 0.036
## 14 M 1.000 1.000
## 15 B 0.350 0.350
## 16 B 0.016 0.016
## 17 M 0.700 0.700
## 18 B 0.024 0.024
## 19 B 0.232 0.232
## 20 B 0.274 0.274
## 21 B 0.000 0.000
## 22 B 0.074 0.074
## 23 B 0.034 0.034
## 24 B 0.298 0.298
## 25 B 0.054 0.054
## 26 M 1.000 1.000
## 27 B 0.738 0.738
## 28 M 0.992 0.992
## 29 B 0.000 0.000
## 30 B 0.024 0.024
## 31 M 0.964 0.964
## 32 M 0.842 0.842
## 33 M 0.666 0.666
## 34 M 0.998 0.998
## 35 B 0.638 0.638
## 36 B 0.012 0.012
## 37 M 0.784 0.784
## 38 B 0.000 0.000
## 39 M 0.900 0.900
## 40 M 1.000 1.000
## 41 M 1.000 1.000
## 42 M 1.000 1.000
## 43 M 0.978 0.978
## 44 B 0.248 0.248
## 45 B 0.266 0.266
## 46 M 0.916 0.916
## 47 M 0.978 0.978
## 48 B 0.592 0.592
## 49 B 0.000 0.000
## 50 B 0.000 0.000
## 51 B 0.012 0.012
## 52 B 0.000 0.000
## 53 M 1.000 1.000
## 54 B 0.304 0.304
## 55 B 0.000 0.000
## 56 B 0.260 0.260
## 57 M 0.542 0.542
## 58 M 1.000 1.000
## 59 M 0.778 0.778
## 60 M 0.930 0.930
## 61 M 0.998 0.998
## 62 B 0.002 0.002
## 63 B 0.000 0.000
## 64 M 0.806 0.806
## 65 B 0.000 0.000
## 66 B 0.162 0.162
## 67 B 0.000 0.000
## 68 B 0.000 0.000
## 69 B 0.004 0.004
## 70 B 0.016 0.016
## 71 B 0.002 0.002
## 72 B 0.142 0.142
## 73 B 0.000 0.000
## 74 B 0.032 0.032
## 75 B 0.002 0.002
## 76 B 0.002 0.002
## 77 B 0.000 0.000
## 78 B 0.000 0.000
## 79 M 1.000 1.000
## 80 B 0.104 0.104
## 81 B 0.002 0.002
## 82 B 0.008 0.008
## 83 B 0.636 0.636
## 84 M 1.000 1.000
## 85 M 0.994 0.994
## 86 B 0.000 0.000
## 87 M 0.958 0.958
## 88 B 0.002 0.002
## 89 B 0.124 0.124
## 90 B 0.044 0.044
## 91 M 0.826 0.826
## 92 B 0.022 0.022
## 93 B 0.004 0.004
## 94 B 0.022 0.022
## 95 B 0.128 0.128
## 96 M 0.982 0.982
## 97 B 0.000 0.000
## 98 B 0.832 0.832
## 99 B 0.008 0.008
## 100 B 0.118 0.118
## 101 M 1.000 1.000
## 102 B 0.020 0.020
## 103 B 0.088 0.088
## 104 B 0.788 0.788
## 105 B 0.002 0.002
## 106 B 0.632 0.632
## 107 B 0.046 0.046
## 108 B 0.738 0.738
## 109 M 0.970 0.970
## 110 M 0.972 0.972
## 111 B 0.376 0.376
## 112 B 0.036 0.036
## 113 B 0.020 0.020
## 114 B 0.268 0.268
## 115 B 0.000 0.000
## 116 B 0.530 0.530
## 117 B 0.062 0.062
## 118 M 0.996 0.996
## 119 M 1.000 1.000
## 120 M 0.914 0.914
## 121 M 0.952 0.952
## 122 B 0.024 0.024
## 123 B 0.000 0.000
## 124 M 1.000 1.000
## 125 M 0.996 0.996
## 126 M 0.884 0.884
## 127 M 0.902 0.902
## 128 B 0.098 0.098
## 129 B 0.014 0.014
## 130 B 0.020 0.020
## 131 M 1.000 1.000
## 132 B 0.008 0.008
## 133 B 0.204 0.204
## 134 M 1.000 1.000
## 135 B 0.000 0.000
## 136 M 1.000 1.000
## 137 B 0.000 0.000
## 138 B 0.200 0.200
## 139 B 0.738 0.738
## 140 M 0.998 0.998
## 141 M 0.964 0.964
## 142 B 0.014 0.014
## 143 M 0.968 0.968
## 144 M 0.974 0.974
## 145 B 0.466 0.466
## 146 B 0.060 0.060
## 147 B 0.004 0.004
## 148 M 0.946 0.946
## 149 M 0.948 0.948
## 150 M 0.934 0.934
## 151 M 0.970 0.970
## 152 M 1.000 1.000
## 153 B 0.000 0.000
## 154 M 0.786 0.786
## 155 M 0.916 0.916
## 156 B 0.592 0.592
## 157 B 0.012 0.012
## 158 B 0.000 0.000
## 159 M 0.994 0.994
## 160 B 0.086 0.086
## 161 B 0.000 0.000
## 162 B 0.600 0.600
## 163 M 0.908 0.908
## 164 M 0.090 0.090
## 165 M 1.000 1.000
## 166 B 0.000 0.000
## 167 B 0.000 0.000
## 168 M 0.666 0.666
## 169 M 0.992 0.992
## 170 B 0.000 0.000
## 171 B 0.000 0.000
## 172 B 0.000 0.000
## 173 B 0.002 0.002
## 174 B 0.000 0.000
## 175 M 0.866 0.866
## 176 B 0.000 0.000
## 177 M 0.942 0.942
## 178 B 0.006 0.006
## 179 B 0.000 0.000
## 180 M 1.000 1.000
## 181 M 0.956 0.956
## 182 B 0.002 0.002
## 183 B 0.000 0.000
## 184 B 0.142 0.142
## 185 M 1.000 1.000
## 186 B 0.000 0.000
## 187 B 0.030 0.030
## 188 B 0.094 0.094
## 189 B 0.000 0.000
## 190 B 0.000 0.000
## 191 B 0.000 0.000
## 192 B 0.000 0.000
## 193 B 0.002 0.002
## 194 B 0.208 0.208
## 195 B 0.790 0.790
## 196 M 1.000 1.000
## 197 B 0.446 0.446
## 198 B 0.028 0.028
## 199 B 0.498 0.498
## 200 B 0.002 0.002
## 201 B 0.160 0.160
## 202 M 0.998 0.998
## 203 B 0.164 0.164
## 204 B 0.006 0.006
## 205 B 0.000 0.000
## 206 B 0.172 0.172
## 207 B 0.000 0.000
## 208 B 0.016 0.016
## 209 B 0.146 0.146
## 210 B 0.000 0.000
## 211 B 0.832 0.832
## 212 M 0.448 0.448
## 213 M 1.000 1.000
## 214 B 0.058 0.058
## 215 B 0.200 0.200
## 216 B 0.216 0.216
## 217 B 0.788 0.788
## 218 B 0.000 0.000
## 219 B 0.000 0.000
## 220 M 0.954 0.954
## 221 M 0.972 0.972
## 222 B 0.202 0.202
## 223 B 0.376 0.376
## 224 B 0.418 0.418
## 225 M 0.932 0.932
## 226 B 0.036 0.036#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_RF_Test_ROC <- roc(response = MEL_RF_Test$MEL_RF_Test_Observed,
predictor = MEL_RF_Test$MEL_RF_Test_Predicted.M,
levels = rev(levels(MEL_RF_Test$MEL_RF_Test_Observed)))
(MEL_RF_Test_AUROC <- auc(MEL_RF_Test_ROC)[1])## [1] 0.98843061.8 Algorithm Comparison Summary
Details.
Code Chunk | Output
##################################
# Consolidating the resampling results
# for the formulated individual models
##################################
(Consolidated_Resampling <- resamples(list(MBS_AB = MBS_AB_Tune,
MBS_GBM = MBS_GBM_Tune,
MBS_XGB = MBS_XGB_Tune,
MBG_RF = MBG_RF_Tune,
MBG_BTREE = MBG_BTREE_Tune,
BAL_LDA = BAL_LDA_Tune,
BAL_CART = BAL_CART_Tune,
BAL_KNN = BAL_KNN_Tune,
BAL_NB = BAL_NB_Tune)))##
## Call:
## resamples.default(x = list(MBS_AB = MBS_AB_Tune, MBS_GBM =
## = MBG_BTREE_Tune, BAL_LDA = BAL_LDA_Tune, BAL_CART = BAL_CART_Tune, BAL_KNN
## = BAL_KNN_Tune, BAL_NB = BAL_NB_Tune))
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BTREE, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
## Performance metrics: ROC, Sens, Spec
## Time estimates for: everything, final model fitsummary(Consolidated_Resampling)##
## Call:
## summary.resamples(object = Consolidated_Resampling)
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BTREE, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.9316305 0.9567775 0.9663683 0.9647554 0.9751032 0.9858101 0
## MBS_GBM 0.9202786 0.9524297 0.9627193 0.9599595 0.9720072 0.9840041 0
## MBS_XGB 0.9176471 0.9510230 0.9667183 0.9589816 0.9720072 0.9847781 0
## MBG_RF 0.9299872 0.9576726 0.9669763 0.9609714 0.9732972 0.9778767 0
## MBG_BTREE 0.9247291 0.9515985 0.9614938 0.9579040 0.9717492 0.9786507 0
## BAL_LDA 0.8184143 0.8556502 0.8810630 0.8736974 0.8914322 0.9135550 0
## BAL_CART 0.8157250 0.8470588 0.8759030 0.8699967 0.8950128 0.9122162 0
## BAL_KNN 0.8355263 0.8916880 0.9033887 0.8999215 0.9180946 0.9486584 0
## BAL_NB 0.8240409 0.8697110 0.8884159 0.8864212 0.9076367 0.9236573 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.7794118 0.8676471 0.9117647 0.9011765 0.9411765 0.9558824 0
## MBS_GBM 0.7352941 0.8823529 0.8970588 0.8970588 0.9264706 0.9705882 0
## MBS_XGB 0.7500000 0.8676471 0.9117647 0.8970588 0.9264706 0.9705882 0
## MBG_RF 0.7941176 0.8529412 0.9117647 0.8970588 0.9411765 0.9705882 0
## MBG_BTREE 0.7794118 0.8676471 0.9117647 0.8976471 0.9264706 0.9558824 0
## BAL_LDA 0.6176471 0.6617647 0.6911765 0.6988235 0.7352941 0.8088235 0
## BAL_CART 0.6617647 0.7205882 0.7647059 0.7600000 0.7941176 0.8823529 0
## BAL_KNN 0.7500000 0.8529412 0.8970588 0.8841176 0.9264706 0.9558824 0
## BAL_NB 0.6617647 0.7205882 0.7500000 0.7576471 0.7941176 0.8676471 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.8947368 0.9210526 0.9385965 0.9398352 0.9649123 0.9739130 0
## MBS_GBM 0.8859649 0.9217391 0.9385965 0.9405370 0.9649123 0.9824561 0
## MBS_XGB 0.9035088 0.9298246 0.9391304 0.9405492 0.9561404 0.9739130 0
## MBG_RF 0.9035088 0.9304348 0.9391304 0.9436949 0.9565217 0.9826087 0
## MBG_BTREE 0.9035088 0.9385965 0.9391304 0.9457818 0.9565217 0.9826087 0
## BAL_LDA 0.8157895 0.8508772 0.8869565 0.8831976 0.9043478 0.9304348 0
## BAL_CART 0.8157895 0.8596491 0.8684211 0.8688787 0.8859649 0.9210526 0
## BAL_KNN 0.8684211 0.8947368 0.9130435 0.9157254 0.9385965 0.9565217 0
## BAL_NB 0.7894737 0.8434783 0.8684211 0.8643356 0.8869565 0.9217391 0##################################
# Exploring the resampling results
# for the formulated individual models
##################################
bwplot(Consolidated_Resampling,
main = "Model Resampling Performance Comparison (Range)",
ylab = "Model",
pch=16,
cex=2,
layout=c(3,1))
##################################
# Consolidating the train and test AUROC
# for the formulated individual models
# together with the ensemble and stacked models
##################################
Model <- c('MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BTREE',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'ENL','MEL_LR','MEL_RF',
'MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BTREE',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'ENL','MEL_LR','MEL_RF')
Set <- c(rep('Cross-Validation',13),rep('Test',13))
AUROC <- c(MBS_AB_Train_AUROC,MBS_GBM_Train_AUROC,MBS_XGB_Train_AUROC,
MBG_RF_Train_AUROC,MBG_BTREE_Train_AUROC,
BAL_LDA_Train_AUROC,BAL_CART_Train_AUROC,BAL_SVM_R_Train_AUROC,BAL_KNN_Train_AUROC,BAL_NB_Train_AUROC,
ENL_Train_AUROC,MEL_LR_Train_AUROC,MEL_RF_Train_AUROC,
MBS_AB_Test_AUROC,MBS_GBM_Test_AUROC,MBS_XGB_Test_AUROC,
MBG_RF_Test_AUROC,MBG_BTREE_Test_AUROC,
BAL_LDA_Test_AUROC,BAL_CART_Test_AUROC,BAL_SVM_R_Test_AUROC,BAL_KNN_Test_AUROC,BAL_NB_Test_AUROC,
ENL_Test_AUROC,MEL_LR_Test_AUROC,MEL_RF_Test_AUROC)
AUROC_Summary <- as.data.frame(cbind(Model,Set,AUROC))
AUROC_Summary$AUROC <- as.numeric(as.character(AUROC_Summary$AUROC))
AUROC_Summary$Set <- factor(AUROC_Summary$Set,
levels = c("Cross-Validation",
"Test"))
AUROC_Summary$Model <- factor(AUROC_Summary$Model,
levels = c('MBS_AB',
'MBS_GBM',
'MBS_XGB',
'MBG_RF',
'MBG_BTREE',
'BAL_LDA',
'BAL_CART',
'BAL_SVM_R',
'BAL_KNN',
'BAL_NB',
'ENL',
'MEL_LR',
'MEL_RF'))
print(AUROC_Summary, row.names=FALSE)## Model Set AUROC
## MBS_AB Cross-Validation 0.9647554
## MBS_GBM Cross-Validation 0.9599595
## MBS_XGB Cross-Validation 0.9589816
## MBG_RF Cross-Validation 0.9609714
## MBG_BTREE Cross-Validation 0.9579040
## BAL_LDA Cross-Validation 0.8736974
## BAL_CART Cross-Validation 0.8699967
## BAL_SVM_R Cross-Validation 0.9095067
## BAL_KNN Cross-Validation 0.8999215
## BAL_NB Cross-Validation 0.8864212
## ENL Cross-Validation 0.9476578
## MEL_LR Cross-Validation 0.9476578
## MEL_RF Cross-Validation 0.9807720
## MBS_AB Test 0.9936284
## MBS_GBM Test 0.9825620
## MBS_XGB Test 0.9830651
## MBG_RF Test 0.9935446
## MBG_BTREE Test 0.9928739
## BAL_LDA Test 0.8984742
## BAL_CART Test 0.8843478
## BAL_SVM_R Test 0.9159121
## BAL_KNN Test 0.9718310
## BAL_NB Test 0.9038397
## ENL Test 0.9862508
## MEL_LR Test 0.9862508
## MEL_RF Test 0.9884306(AUROC_Plot <- dotplot(Model ~ AUROC,
data = AUROC_Summary,
groups = Set,
main = "Classification Model Performance Comparison",
ylab = "Model",
xlab = "AUROC",
auto.key = list(adj = 1),
type=c("p", "h"),
origin = 0,
alpha = 0.45,
pch = 16,
cex = 2))
2. References
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
[Book] Multivariate Data Visualization with R by Deepayan Sarkar
[Book] Machine Learning by Samuel Jackson
[Book] Data Modeling Methods by Jacob Larget
[Book] Introduction to R and Statistics by University of Western Australia
[Book] Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
[Book] Introduction to Research Methods by Eric van Holm
[R Package] AppliedPredictiveModeling by Max Kuhn
[R Package] caret by Max Kuhn
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] lattice by Deepayan Sarkar
[R Package] dplyr by Hadley Wickham
[R Package] tidyr by Hadley Wickham
[R Package] moments by Lukasz Komsta and Frederick
[R Package] skimr by Elin Waring
[R Package] RANN by Sunil Arya, David Mount, Samuel Kemp and Gregory Jefferis
[R Package] corrplot by Taiyun Wei
[R Package] tidyverse by Hadley Wickham
[R Package] lares by Bernardo Lares
[R Package] DMwR by Luis Torgo
[R Package] gridExtra by Baptiste Auguie and Anton Antonov
[R Package] rattle by Graham Williams
[R Package] RColorBrewer by Erich Neuwirth
[R Package] stats by R Core Team
[R Package] caretEnsemble
by Zachary Deane-Mayer
[Article] A Brief Introduction to caretEnsemble) by Zachary Deane-Mayer
[Article] How to Build an Ensemble Of Machine Learning Algorithms in R by Jason Brownlee
[Article] Ensemble Learning: Bagging, Boosting, and Stacking by Towards AI Team
[Article] Bagging, Boosting, and Stacking in Machine Learning by Emmanuella Budu
[Article] Stacking Ensemble Machine Learning With Python by Jason Brownlee
[Article] Essence of Boosting Ensembles for Machine Learning by Jason Brownlee
[Article] Ensemble Modeling with R by Deepika Singh
[Article] Creating Ensemble Models in R by Dustin Rogers
[Publication] Ensemble Selection from Libraries of Models by Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew and Alex Ksikes (Proceedings of the 21 st International Conference on Machine Learning,)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science
[Article] A Brief Introduction to caretEnsemble) by Zachary Deane-Mayer
[Article] How to Build an Ensemble Of Machine Learning Algorithms in R by Jason Brownlee
[Article] Ensemble Learning: Bagging, Boosting, and Stacking by Towards AI Team
[Article] Bagging, Boosting, and Stacking in Machine Learning by Emmanuella Budu
[Article] Stacking Ensemble Machine Learning With Python by Jason Brownlee
[Article] Essence of Boosting Ensembles for Machine Learning by Jason Brownlee
[Article] Ensemble Modeling with R by Deepika Singh
[Article] Creating Ensemble Models in R by Dustin Rogers
[Publication] Ensemble Selection from Libraries of Models by Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew and Alex Ksikes (Proceedings of the 21 st International Conference on Machine Learning,)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science